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    <h1>2012 and 2016 Presidential Elections</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/elections1216.R'><code>R/elections1216.R</code></a></small>
    <div class="hidden name"><code>elections1216.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>US Presidential Election results in 2012 and 2016, by county.</p>
    </div>

    <pre class="usage"><span class='va'>elections1216</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 3108 rows, 74 variables, and a geometry column:</p><dl>
<dt>	STATEFP	</dt><dd><p>State FIPS code</p></dd>
<dt>	COUNTYFP	</dt><dd><p>County FIPS code</p></dd>
<dt>	GEOID	</dt><dd><p>Geographic area ID</p></dd>
<dt>	ALAND	</dt><dd><p>Land area</p></dd>
<dt>	AWATER	</dt><dd><p>Water area</p></dd>
<dt>	area_name	</dt><dd><p>County name</p></dd>
<dt>	state_abbr	</dt><dd><p>State abbreviation</p></dd>
<dt>	PST045214	</dt><dd><p>Population, 2014 estimate</p></dd>
<dt>	PST040210	</dt><dd><p>Population, 2010 (April 1) estimates base</p></dd>
<dt>	PST120214	</dt><dd><p>Population percentage change - April 1, 2010 to July 1, 2014</p></dd>
<dt>	POP010210	</dt><dd><p>Population, 2010</p></dd>
<dt>	AGE135214	</dt><dd><p>Percentage of persons under 5 years, 2014</p></dd>
<dt>	AGE295214	</dt><dd><p>Percentage of persons under 18 years, 2014</p></dd>
<dt>	AGE775214	</dt><dd><p>Persons 65 years and over, percentage, 2014</p></dd>
<dt>	SEX255214	</dt><dd><p>Percentage of female persons, 2014</p></dd>
<dt>	RHI125214	</dt><dd><p>Percentage of white alone, 2014</p></dd>
<dt>	RHI225214	</dt><dd><p>Percentage of black or African American alone, 2014</p></dd>
<dt>	RHI325214	</dt><dd><p>Percentage of American Indian and Alaska Native alone, 2014</p></dd>
<dt>	RHI425214	</dt><dd><p>Percentage of Asian alone percentage, 2014</p></dd>
<dt>	RHI525214	</dt><dd><p>Percentage of Native Hawaiian and Other Pacific Islander alone, 2014</p></dd>
<dt>	RHI625214	</dt><dd><p>Percentage of two or More Races, 2014</p></dd>
<dt>	RHI725214	</dt><dd><p>Percentage of Hispanic or Latino, 2014</p></dd>
<dt>	RHI825214	</dt><dd><p>Percentage of White alone, not Hispanic or Latino, 2014</p></dd>
<dt>	POP715213	</dt><dd><p>Living in same house 1 year &amp; over, percentage, 2009-2013</p></dd>
<dt>	POP645213	</dt><dd><p>Foreign born persons, percentage, 2009-2013</p></dd>
<dt>	POP815213	</dt><dd><p>Language other than English spoken at home, pct age 5+, 2009-2013</p></dd>
<dt>	EDU635213	</dt><dd><p>High school graduate or higher, percentage of persons age 25+, 2009-2013</p></dd>
<dt>	EDU685213	</dt><dd><p>Bachelor’s degree or higher, percentage of persons age 25+, 2009-2013</p></dd>
<dt>	VET605213	</dt><dd><p>Veterans, 2009-2013</p></dd>
<dt>	LFE305213	</dt><dd><p>Mean travel time to work (minutes), workers age 16+, 2009-2013</p></dd>
<dt>	HSG010214	</dt><dd><p>Housing units, 2014</p></dd>
<dt>	HSG445213	</dt><dd><p>Homeownership rate, 2009-2013</p></dd>
<dt>	HSG096213	</dt><dd><p>Housing units in multi-unit structures, percentage, 2009-2013</p></dd>
<dt>	HSG495213	</dt><dd><p>Median value of owner-occupied housing units, 2009-2013</p></dd>
<dt>	HSD410213	</dt><dd><p>Households, 2009-2013</p></dd>
<dt>	HSD310213	</dt><dd><p>Persons per household, 2009-2013</p></dd>
<dt>	INC910213	</dt><dd><p>Per capita money income in past 12 months (2013 dollars), 2009-2013</p></dd>
<dt>	INC110213	</dt><dd><p>Median household income, 2009-2013</p></dd>
<dt>	PVY020213	</dt><dd><p>Persons below poverty level, percentage, 2009-2013</p></dd>
<dt>	BZA010213	</dt><dd><p>Private nonfarm establishments, 2013</p></dd>
<dt>	BZA110213	</dt><dd><p>Private nonfarm employment, 2013</p></dd>
<dt>	BZA115213	</dt><dd><p>Private nonfarm employment, percentage change, 2012-2013</p></dd>
<dt>	NES010213	</dt><dd><p>Nonemployer establishments, 2013</p></dd>
<dt>	SBO001207	</dt><dd><p>Total number of firms, 2007</p></dd>
<dt>	SBO315207	</dt><dd><p>Black-owned firms, percentage, 2007</p></dd>
<dt>	SBO115207	</dt><dd><p>American Indian- and Alaska Native-owned firms, percentage, 2007</p></dd>
<dt>	SBO215207	</dt><dd><p>Asian-owned firms, percentage, 2007</p></dd>
<dt>	SBO515207	</dt><dd><p>Native Hawaiian- and Other Pacific Islander-owned firms, percentage, 2007</p></dd>
<dt>	SBO415207	</dt><dd><p>Hispanic-owned firms, percentage, 2007</p></dd>
<dt>	SBO015207	</dt><dd><p>Women-owned firms, percentage, 2007</p></dd>
<dt>	MAN450207	</dt><dd><p>Manufacturers shipments, 2007 ($1,000)</p></dd>
<dt>	WTN220207	</dt><dd><p>Merchant wholesaler sales, 2007 ($1,000)</p></dd>
<dt>	RTN130207	</dt><dd><p>Retail sales, 2007 ($1,000)</p></dd>
<dt>	RTN131207	</dt><dd><p>Retail sales per capita, 2007</p></dd>
<dt>	AFN120207	</dt><dd><p>Accommodation and food services sales, 2007 ($1,000)</p></dd>
<dt>	BPS030214	</dt><dd><p>Building permits, 2014</p></dd>
<dt>	LND110210	</dt><dd><p>Land area in square miles, 2010</p></dd>
<dt>	POP060210	</dt><dd><p>Population per square mile, 2010</p></dd>
<dt>	Demvotes16	</dt><dd><p>Votes for Democratic candidate in 2016 presidential election</p></dd>
<dt>	GOPvotes16	</dt><dd><p>Votes for Republican candidate in 2016 presidential election</p></dd>
<dt>	total_2016	</dt><dd><p>Total number of votes cast in 2016 presidential election</p></dd>
<dt>	pct_dem_16	</dt><dd><p>Votes for Democratic candidate as percentage of total votes</p></dd>
<dt>	pct_gop_16	</dt><dd><p>Votes for Republican candidate as percentage of total votes</p></dd>
<dt>	diff_2016	</dt><dd><p>Difference between the number of votes for Republican and Democratic candidates</p></dd>
<dt>	pct_pt_16	</dt><dd><p>The number in diff_2016 expressed as a percentage of the total votes. Negative if fewer votes were cast for the Democratic candidate</p></dd>
<dt>	total_2012	</dt><dd><p>Total number of votes cast in 2012 presidential election</p></dd>
<dt>	Demvotes12	</dt><dd><p>Votes for Democratic candidate as percentage of total votes</p></dd>
<dt>	GOPvotes12	</dt><dd><p>Votes for Republican candidate as percentage of total votes</p></dd>
<dt>	pct_dem_12	</dt><dd><p>Votes for Democratic candidate as percentage of total votes</p></dd>
<dt>	pct_gop_12	</dt><dd><p>Votes for Republican candidate as percentage of total votes</p></dd>
<dt>	diff_2012	</dt><dd><p>Difference between the number of votes for Republican and Democratic candidates</p></dd>
<dt>	pct_pt_12	</dt><dd><p>The number in diff_2012 expressed as a percentage of the total votes. Negative if fewer votes were cast for the Democratic candidate</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p><a href='https://github.com/tonmcg/US_County_Level_Election_Results_08-16'>https://github.com/tonmcg/US_County_Level_Election_Results_08-16</a> and <a href='https://www.kaggle.com/benhamner/2016-us-election'>https://www.kaggle.com/benhamner/2016-us-election</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 102434: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>elections1216</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>elections1216</span><span class='op'>[</span><span class='st'>"diff_2012"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='elections1216-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>International Aid to Nepal (2007-14)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/nepal.R'><code>R/nepal.R</code></a></small>
    <div class="hidden name"><code>nepal.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Contains development-related data for 75 districts in Nepal.</p>
    </div>

    <pre class="usage"><span class='va'>nepal</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 75 rows, 61 variables, and a geometry column:</p><dl>
<dt>	id	</dt><dd><p>Unique ID</p></dd>
<dt>	region	</dt><dd><p>Nepal region</p></dd>
<dt>	zone	</dt><dd><p>Nepal zone</p></dd>
<dt>	district	</dt><dd><p>Nepal district</p></dd>
<dt>	depecprov	</dt><dd><p>Deprivation in economic provisioning</p></dd>
<dt>	povindex	</dt><dd><p>Human Poverty Index</p></dd>
<dt>	pcinc	</dt><dd><p>Per Capita Income</p></dd>
<dt>	pcincppp	</dt><dd><p>Per Capita Income PPP</p></dd>
<dt>	pcincmp	</dt><dd><p>Per capita income, Rs. at market price</p></dd>
<dt>	malkids	</dt><dd><p>Percentage of children under age five who are malnourished</p></dd>
<dt>	lif40	</dt><dd><p>Percentage of People not expected to survive age 40</p></dd>
<dt>	nosafh20	</dt><dd><p>Percentage without safe water</p></dd>
<dt>	population	</dt><dd><p>Population</p></dd>
<dt>	boyg1_5	</dt><dd><p>Number of Boys Enrolled in Grade 1-5 (2012-2013)</p></dd>
<dt>	girlg1_5	</dt><dd><p>Number of Girls Enrolled in Grade 1-5 (2012-2013)</p></dd>
<dt>	kids1_5	</dt><dd><p>Number of Children Enrolled in Grade 1-5 (2012-2013)</p></dd>
<dt>	schoolcnt	</dt><dd><p>Number of Schools (2012-2013)</p></dd>
<dt>	schlpkid	</dt><dd><p>Number of Schools per child (in thousands) (2012-2013)</p></dd>
<dt>	schlppop	</dt><dd><p>Number of Schools per population (in thousands) (2012-2013)</p></dd>
<dt>	ad_illit	</dt><dd><p>Adult illiteracy rate (2011)</p></dd>
<dt>	ad_ilgt50	</dt><dd><p>Dummy variable with value of 1 if adult illiteracy rate &gt;50pct (2011)</p></dd>
<dt>	lon	</dt><dd><p>Longitude</p></dd>
<dt>	lat	</dt><dd><p>Latitude</p></dd>
<dt>	xxCAMT	</dt><dd><p>Project Sector Committed Amount: xx = sector: Agriculture (AG), Business Banking (BANK), Communication (COMM), Conflict Resolution (CON), Education (EDU), Energy (ENGY), Environment (ENV), Forestry (FOR), Government and Civil Society (GOV), Health (HEALT), Humanitarian Aid (HUM), Industry (IND), Multi-Sector (MUL), Social Infrastructure (SOC), Tourism (TOUR), Transport and Storage (TRAN), Water Sanitation (WAT), Total (TOT)</p></dd>
<dt>	xxDAMT	</dt><dd><p>Project Sector Distributed Amount by sector xx (see above)</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>Documentation for original data: AidData (1997-2014 with most projects from 2007-14) and <a href='http://data.opennepal.net/'>http://data.opennepal.net/</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>nepal</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>nepal</span><span class='op'>[</span><span class='st'>"kids1_5"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='nepal-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>2000 Health, Income and Diversity</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/healthplus.R'><code>R/healthplus.R</code></a></small>
    <div class="hidden name"><code>healthplus.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Income, race, and public health statistics for US counties.</p>
    </div>

    <pre class="usage"><span class='va'>healthplus</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 3984 rows, 64 variables, and a geometry column:</p><dl>
<dt>	cartodb_id	</dt><dd><p>Carto ID</p></dd>
<dt>	countyfp	</dt><dd><p>County FIPS ID</p></dd>
<dt>	statefp	</dt><dd><p>State FIPS code</p></dd>
<dt>	statename	</dt><dd><p>State name</p></dd>
<dt>	countyname	</dt><dd><p>County name and State</p></dd>
<dt>	countyfips	</dt><dd><p>County FIPS code</p></dd>
<dt>	ratio	</dt><dd><p>The county’s median income divided by the state’s median income</p></dd>
<dt>	cty_pop200	</dt><dd><p>County’s population in 2000</p></dd>
<dt>	le_agg_q1	</dt><dd><p>Female life expectancy, 1st Income Quartile, not adjusted for race.</p></dd>
<dt>	le_raceadj	</dt><dd><p>Female life expectancy, 1st Income Quartile, adjusted for race.</p></dd>
<dt>	le_agg_q2	</dt><dd><p>Female life expectancy, 2nd Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_1	</dt><dd><p>Female life expectancy, 2nd Income Quartile, adjusted for race</p></dd>
<dt>	le_agg_q3	</dt><dd><p>Female life expectancy, 3rd Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_2	</dt><dd><p>Female life expectancy, 3rd Income Quartile, adjusted for race</p></dd>
<dt>	le_agg_q4	</dt><dd><p>Female life expectancy, 4th Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_3	</dt><dd><p>Female life expectancy, 4th Income Quartile, adjusted for race</p></dd>
<dt>	le_agg_q11	</dt><dd><p>Male life expectancy, 1st Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_4	</dt><dd><p>Male life expectancy, 1st Income Quartile, adjusted for race</p></dd>
<dt>	le_agg_q21	</dt><dd><p>Male life expectancy, 2nd Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_5	</dt><dd><p>Male life expectancy, 2nd Income Quartile, adjusted for race</p></dd>
<dt>	le_agg_q31	</dt><dd><p>Male life expectancy, 3rd Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_6	</dt><dd><p>Male life expectancy, 3rd Income Quartile, adjusted for race</p></dd>
<dt>	le_agg_q41	</dt><dd><p>Male life expectancy, 4th Income Quartile, not adjusted for race</p></dd>
<dt>	le_racea_7	</dt><dd><p>Male life expectancy, 4th Income Quartile, adjusted for race</p></dd>
<dt>	sd_le_agg	</dt><dd><p>Standard error for female life expectancy, 1st income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_race	</dt><dd><p>Standard error for female life expectancy, 1st income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_agg1	</dt><dd><p>Standard error for female life expectancy, 2nd income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_1	</dt><dd><p>Standard error for female life expectancy, 2nd income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_ag_1	</dt><dd><p>Standard error for female life expectancy, 3rd income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_2	</dt><dd><p>Standard error for female life expectancy, 3rd income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_ag_2	</dt><dd><p>Standard error for female life expectancy, 4th income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_3	</dt><dd><p>Standard error for female life expectancy, 4th income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_ag_3	</dt><dd><p>Standard error for male life expectancy, 1st income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_4	</dt><dd><p>Standard error for male life expectancy, 1st income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_ag_4	</dt><dd><p>Standard error for male life expectancy, 2nd income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_5	</dt><dd><p>Standard error for male life expectancy, 2nd income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_ag_5	</dt><dd><p>Standard error for male life expectancy, 3rd income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_6	</dt><dd><p>Standard error for male life expectancy, 3rd income Quartile, adjusted for race.</p></dd>
<dt>	sd_le_ag_6	</dt><dd><p>Standard error for male life expectancy, 4th income Quartile, not adjusted for race.</p></dd>
<dt>	sd_le_ra_7	</dt><dd><p>Standard error for male life expectancy, 4th income Quartile, adjusted for race.</p></dd>
<dt>	count_q1_F	</dt><dd><p>Female count, 1st income quartile.</p></dd>
<dt>	count_q2_F	</dt><dd><p>Female count, 2nd income quartile.</p></dd>
<dt>	count_q3_F	</dt><dd><p>Female count, 3rd income quartile.</p></dd>
<dt>	count_q4_F	</dt><dd><p>Female count, 4th income quartile.</p></dd>
<dt>	count_q1_M	</dt><dd><p>Male count, 1st income quartile.</p></dd>
<dt>	count_q2_M	</dt><dd><p>Male count, 2nd income quartile.</p></dd>
<dt>	count_q3_M	</dt><dd><p>Male count, 3rd income quartile.</p></dd>
<dt>	count_q4_M	</dt><dd><p>Male count, 4th income quartile.</p></dd>
<dt>	Diversity	</dt><dd><p>Diversity Index</p></dd>
<dt>	Asianalon	</dt><dd><p>Asian alone, percent</p></dd>
<dt>	NativeHaw	</dt><dd><p>Native Hawaiian, percent</p></dd>
<dt>	TwoorMor	</dt><dd><p>Two or more races, percent</p></dd>
<dt>	Hispanico	</dt><dd><p>Hispanic, percent</p></dd>
<dt>	Whitealon	</dt><dd><p>White alone, percent</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>Multiple sources: (1) ratio from <a href='https://philpierdo2.carto.com/me'>https://philpierdo2.carto.com/me</a>, (2) race and diversity index source: <a href='https://www.kaggle.com/mikejohnsonjr/us-counties-diversity-index'>https://www.kaggle.com/mikejohnsonjr/us-counties-diversity-index</a> and (3) income and life expectancy variables obtained from: Chetty, Stepner, Abraham, Lin, Scuderi, Turner, Bergeron, and Cutler (2016). The Association Between Income and Life Expectancy in the United States, 2001-2014. Health Statistics by County.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>healthplus</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>healthplus</span><span class='op'>[</span><span class='st'>"le_agg_q1"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='healthplus-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>Chicago Health Indicators (2005-11)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/chihealth.R'><code>R/chihealth.R</code></a></small>
    <div class="hidden name"><code>chihealth.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Health indicators for Chicago neighorhoods.</p>
    </div>

    <pre class="usage"><span class='va'>chihealth</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 77 rows, 31 variables, and a geometry column:</p><dl>
<dt>	area_num	</dt><dd><p>Community area ID</p></dd>
<dt>	shape_area	</dt><dd><p>Polygon area</p></dd>
<dt>	shape_len	</dt><dd><p>Shape length</p></dd>
<dt>	comm_area	</dt><dd><p>Community area name</p></dd>
<dt>	BirthRate	</dt><dd><p>Births per 1,000 residents</p></dd>
<dt>	Gener_Rate	</dt><dd><p>General fertility rate per 1,000 females 15-44</p></dd>
<dt>	LowBi_ight	</dt><dd><p>Percentage of live births with low birth weight</p></dd>
<dt>	Prena_ster	</dt><dd><p>Percentage of live births who had prenatal care beginning in first trimester</p></dd>
<dt>	Prete_rths	</dt><dd><p>Preterm births as percentage of live births</p></dd>
<dt>	TeenB_Rate	</dt><dd><p>Teen birth rate per 1,000 females age 15-19</p></dd>
<dt>	Assa_cide	</dt><dd><p>Assault(homicide) per 100,000 people, age adjusted.</p></dd>
<dt>	Breas_ales	</dt><dd><p>Breast cancer deaths per 100,000 females, age adjusted.</p></dd>
<dt>	Cance_ites	</dt><dd><p>Cancer deaths per 100,000 people, age adjusted.</p></dd>
<dt>	Color_ncer	</dt><dd><p>Colorectal cancer deaths per 100,000 people, age adjusted.</p></dd>
<dt>	Diabe_ated	</dt><dd><p>Diabetes-related deaths per 100,000 people, age adjusted.</p></dd>
<dt>	Firea_ated	</dt><dd><p>Firearm related deaths per 1000,000 people, age adjusted.</p></dd>
<dt>	Infan_Rate	</dt><dd><p>Infant mortality rate per 1,000 live births</p></dd>
<dt>	LungCancer	</dt><dd><p>Lung cancer deaths per 100,000 people, age adjusted.</p></dd>
<dt>	Prost_ales	</dt><dd><p>Prostate cancer deaths per 100,000 males, age adjusted.</p></dd>
<dt>	Strok_ease	</dt><dd><p>Stroke deaths per 100,000 people, age adjusted.</p></dd>
<dt>	Child_ning	</dt><dd><p>Childhood blood lead level screening per 1,000 children age 0-6 years</p></dd>
<dt>	Chil_ing_1	</dt><dd><p>Childhood lead poisoning per 100</p></dd>
<dt>	Gonor_ales	</dt><dd><p>Gonorrhea in females, per 100,000 females age 15-44</p></dd>
<dt>	Gono_ales_1	</dt><dd><p>Gonorrhea in females, per 100,000 females age 15-44</p></dd>
<dt>	Tuber_osis	</dt><dd><p>Tuberculosis per 100,000 people</p></dd>
<dt>	Below_evel	</dt><dd><p>Percentage of households below poverty level</p></dd>
<dt>	Crowd_sing	</dt><dd><p>Percentage of occupied housing units</p></dd>
<dt>	Dependency	</dt><dd><p>Percentage of persons aged less than 16 or more than 64 years</p></dd>
<dt>	NoHig_loma	</dt><dd><p>Percentage of persons age 25 or higher without high school diploma</p></dd>
<dt>	PerCa_come	</dt><dd><p>Percapita income, 2011 adjusted dollars</p></dd>
<dt>	Unemp_ment	</dt><dd><p>Unemployment as a percentage of persons aged 16 or older</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>City of Chicago Data Portal (<a href='https://data.cityofchicago.org/Health-Human-Services/Public-Health-Statistics-Selected-public-health-in/iqnk-2tcu'>https://data.cityofchicago.org/Health-Human-Services/Public-Health-Statistics-Selected-public-health-in/iqnk-2tcu</a>) Data provided by Illinois Department of Public Health (IDPH) and U.S. Census Bureau.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>chihealth</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>chihealth</span><span class='op'>[</span><span class='st'>"Strok_ease"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='chihealth-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>NYC Education (2000)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/nyceducation.R'><code>R/nyceducation.R</code></a></small>
    <div class="hidden name"><code>nyceducation.Rd</code></div>
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    <div class="ref-description">
    <p>Block-level New York City information about education and demographic characteristics (2000). Geographic units: 2010 Census blocks.</p>
    </div>

    <pre class="usage"><span class='va'>nyceducation</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 2,216 rows, 56 variables, and a geometry column:</p><dl>
<dt>	poly_id	</dt><dd><p>Unique ID</p></dd>
<dt>	youth_drop	</dt><dd><p>Percentage of population age 16-19 that has dropped out of high school</p></dd>
<dt>	per_mnrty	</dt><dd><p>Percentage of the population that is non-white</p></dd>
<dt>	hs_drop	</dt><dd><p>Percentage of population age over25 that dropped out of high school</p></dd>
<dt>	col_degree	</dt><dd><p>Percentage of population over age 25 that obtained at least a bachelor’s degree</p></dd>
<dt>	per_asian	</dt><dd><p>Percentage of Asian population</p></dd>
<dt>	per_black	</dt><dd><p>Percentage of black population</p></dd>
<dt>	per_white	</dt><dd><p>Percentage of white population</p></dd>
<dt>	ctlabel	</dt><dd><p>Census tract label</p></dd>
<dt>	borocode	</dt><dd><p>Borough code</p></dd>
<dt>	boroname	</dt><dd><p>Borough name</p></dd>
<dt>	boroct2000	</dt><dd><p>Census tract coding</p></dd>
<dt>	ntacode	</dt><dd><p>Neighborhood tabulation area code</p></dd>
<dt>	ntaname	</dt><dd><p>Neighborhood tabulation area name</p></dd>
<dt>	puma	</dt><dd><p>Public use microarea code</p></dd>
<dt>	shape_leng	</dt><dd><p>Length of polygon border</p></dd>
<dt>	shape_area	</dt><dd><p>Area of polygon</p></dd>
<dt>	np_ct	</dt><dd><p>Number of nonprofits</p></dd>
<dt>	mean_inc	</dt><dd><p>Mean income</p></dd>
<dt>	pop1619	</dt><dd><p>Population age 16-19</p></dd>
<dt>	dropout	</dt><dd><p>High school dropouts, age 16-19</p></dd>
<dt>	enrollhs	</dt><dd><p>Population enrolled in high school, age 16-19</p></dd>
<dt>	per_prv_sc	</dt><dd><p>Percentage of all students Population enrolled in private school</p></dd>
<dt>	per_pub_sc	</dt><dd><p>Percentage of all students Population enrolled in public school</p></dd>
<dt>	over3	</dt><dd><p>Population over 3-year-old</p></dd>
<dt>	notenroll	</dt><dd><p>Population over 3-year-old, not Population enrolled in school</p></dd>
<dt>	over3enroll	</dt><dd><p>Population over 3-year-old, Population enrolled in school</p></dd>
<dt>	pubsch	</dt><dd><p>Population enrolled in public school</p></dd>
<dt>	pub_pk	</dt><dd><p>Population enrolled in public pre-k</p></dd>
<dt>	pub_k8	</dt><dd><p>Population enrolled in public k-8</p></dd>
<dt>	pub_hs	</dt><dd><p>Population enrolled in public high school</p></dd>
<dt>	pub_col	</dt><dd><p>Population enrolled in public college</p></dd>
<dt>	privsch	</dt><dd><p>Population enrolled in private school</p></dd>
<dt>	priv_pk	</dt><dd><p>Population enrolled in private pre-k</p></dd>
<dt>	priv_k8	</dt><dd><p>Population enrolled in private k-8</p></dd>
<dt>	priv_hs	</dt><dd><p>Population enrolled in private high school</p></dd>
<dt>	priv_col	</dt><dd><p>Population enrolled in private college</p></dd>
<dt>	over25	</dt><dd><p>Population over 25 years</p></dd>
<dt>	subhs	</dt><dd><p>Population over 25 years with less than high school degree</p></dd>
<dt>	hs	</dt><dd><p>Population over 25 years with high school degree</p></dd>
<dt>	somecol	</dt><dd><p>Population over 25 years with some college</p></dd>
<dt>	college	</dt><dd><p>Population over 25 years with bachelor’s degree</p></dd>
<dt>	master	</dt><dd><p>Population over 25 years with master’s degree</p></dd>
<dt>	prof	</dt><dd><p>Population over 25 years with professional degree</p></dd>
<dt>	phd	</dt><dd><p>Population over 25 years with phd</p></dd>
<dt>	white	</dt><dd><p>Total white population</p></dd>
<dt>	black	</dt><dd><p>Total black population</p></dd>
<dt>	asian	</dt><dd><p>Total Asian population</p></dd>
<dt>	sub18	</dt><dd><p>Total population under 18 years old</p></dd>
<dt>	gender_par	</dt><dd><p>Gender parity, 1=parity, higher = more males, lower = more females</p></dd>
<dt>	male	</dt><dd><p>Male population</p></dd>
<dt>	female	</dt><dd><p>Female population</p></dd>
<dt>	school_ct	</dt><dd><p>Number of schools</p></dd>
<dt>	popdens	</dt><dd><p>Population density (people per square mile)</p></dd>
<dt>	population	</dt><dd><p>Population count</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>Source for school locations: Open Data New York (<a href='https://data.cityofnewyork.us/Education/School-Point-Locations/jfju-ynrr'>https://data.cityofnewyork.us/Education/School-Point-Locations/jfju-ynrr</a>). Demographics source: 2000 Census.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, Projection 2203. NAD83 / New York Long Island (ftUS)</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>nyceducation</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>nyceducation</span><span class='op'>[</span><span class='st'>"phd"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='nyceducation-1.png' alt='' width='700' height='433' /></div></pre>
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<a href="#geodadata" class="anchor"></a>geodaData</h1></div>
<!-- badges: start -->
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<p>The goal of geodaData is to store sample spatial datasets. These datasets are intended to be used to teach basic spatial analysis concepts. They are used in the weekly R Spatial Workshop at the Center for Spatial Data Science at UChicago, and are based off of the <a href="https://geodacenter.github.io/documentation.html">GeoDa workbook</a> and <a href="https://geodacenter.github.io/data-and-lab/">data site</a> developed by <NAME> and team. Datasets are stored in the sf spatial object format.</p>
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<h2 class="hasAnchor">
<a href="#installation" class="anchor"></a>Installation</h2>
<p>You can install geodaData from CRAN with:</p>
<div class="sourceCode" id="cb1"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"geodaData"</span><span class="op">)</span></pre></div>
<p>You can install the development version of geodaData from <a href="https://github.com/">GitHub</a> with:</p>
<div class="sourceCode" id="cb2"><pre class="downlit">
<span class="co"># install.packages("remotes")</span>
<span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html">install_github</a></span><span class="op">(</span><span class="st">"spatialanalysis/geodaData"</span><span class="op">)</span></pre></div>
</div>
<div id="usage" class="section level2">
<h2 class="hasAnchor">
<a href="#usage" class="anchor"></a>Usage</h2>
<p>To use geodaData in a workshop, first load <code>sf</code>, then load the package:</p>
<div class="sourceCode" id="cb3"><pre class="downlit">
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://r-spatial.github.io/sf/">sf</a></span><span class="op">)</span> <span class="co"># can use without sf, but datasets will print weirdly in console</span>
<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/spatialanalysis/geodaData">geodaData</a></span><span class="op">)</span></pre></div>
<p>Find a list of all datasets in geodaData with:</p>
<div class="sourceCode" id="cb4"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span><span class="op">(</span>package <span class="op">=</span> <span class="st">"geodaData"</span><span class="op">)</span></pre></div>
<p>You can load a specific dataset into your R environment and show the metadata for it:</p>
<div class="sourceCode" id="cb5"><pre class="downlit">
<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">nyc</span><span class="op">)</span>
<span class="op">?</span><span class="va">nyc</span></pre></div>
<p>Instructions for adding a new dataset to this package can be found in the <a href="https://github.com/spatialanalysis/geodaData/wiki/How-to-add-a-dataset-to-this-package">Wiki</a> of this repository.</p>
</div>
<div id="datasets" class="section level2">
<h2 class="hasAnchor">
<a href="#datasets" class="anchor"></a>Datasets</h2>
<p>Current datasets included in this package:</p>
<ul>
<li>
<code>abandonedcars</code>: Abandoned Vehicles (2016).</li>
<li>
<code>airbnb</code>: Airbnb rentals, socioeconomics, and crime in Chicago.</li>
<li>
<code>atlanta_homicide</code>: Atlanta, GA region homicide counts and rates.</li>
<li>
<code>baltimore_home</code>: Baltimore house sales prices and hedonics.</li>
<li>
<code>bostonhsg</code>: Boston housing and neighborhood data.</li>
<li>
<code>buenosaires</code>: Electoral Data for 1999 Argentinean Elections.</li>
<li>
<code>charleston1</code>: 2000 Census Tract Data for Charleston, SC MSA and counties.</li>
<li>
<code>charleston2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) in Charleston, SC MSA.</li>
<li>
<code>chigroceries</code>: Chicago supermarkets (2015).</li>
<li>
<code>chihealth</code>: Chicago Health and Socio-Economics (2014).</li>
<li>
<code>chilelabor</code>: Labor Markets in Chile (1982-2002).</li>
<li>
<code>chisociohealth</code>: Chicago Health Indicators (2005-11).</li>
<li>
<code>cincinnati</code>: Cincinnati Crime and Socio-Demographics (2008).</li>
<li>
<code>cleveland</code>: Cleveland Home Sales (2015).</li>
<li>
<code>columbus</code>: Columbus neighborhood crime (1980).</li>
<li>
<code>commpop</code>: Chicago Population Change (2000-2010).</li>
<li>
<code>community_pop</code>: Chicago Community Area Population for 2010 (non-spatial, csv).</li>
<li>
<code>denver</code>: Demographics and housing in Denver neighborhoods (2010).</li>
<li>
<code>elections1216</code>: 2012 and 2016 Presidential Elections in the United States.</li>
<li>
<code>grid100</code>: Grids with simulated variables.</li>
<li>
<code>guerry</code>: Guerry “Moral Statistics” (1830s).</li>
<li>
<code>healthplus</code>: Health, Income and Diversity for US counties (2000).</li>
<li>
<code>hickory1</code>: Census Tract Data for Hickory, NC MSA and counties (2000).</li>
<li>
<code>hickory2</code>: Zip Code Business Patterns (Census Bureau) for Hickory, NC MSA (1998 and 2001).</li>
<li>
<code>houstonhom</code>: Houston, TX region homicide counts and rates (1980s-90s).</li>
<li>
<code>juvenile</code>: Cardiff juvenile delinquent residences.</li>
<li>
<code>kchomesale</code>: Home Sales in King County, WA (2014-15).</li>
<li>
<code>lansing1</code>: 2000 Census Tract Data for Lansing, MI MSA and counties.</li>
<li>
<code>lansing2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Lansing, MI MSA.</li>
<li>
<code>laozone</code>: Ozone measures at monitoring stations in Los Angeles basin (1996).</li>
<li>
<code>lasrosas1</code>: Corn yield, fertilizer and field data for precision agriculture, Argentina (1999).</li>
<li>
<code>lasrosas2</code>: Corn yield, fertilizer and field data for precision agriculture, Argentina (1999).</li>
<li>
<code>liquorstore</code>: Chicago Liquor Stores (2015).</li>
<li>
<code>malaria_col_dept</code>: Malaria incidence and population (1973, 95, 93 censuses and projections until 2005).</li>
<li>
<code>malaria_col_munic</code>: Malaria incidence and population (1973, 95, 93 censuses and projections until 2005).</li>
<li>
<code>milwaukee1</code>: 2000 Census Tract Data for Milwaukee, WI MSA..</li>
<li>
<code>milwaukee2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Milwaukee, WI MSA.</li>
<li>
<code>mspolice</code>: Police expenditures Mississippi counties.</li>
<li>
<code>ncovr</code>: Homicides &amp; Socio-Economics (1960-90).</li>
<li>
<code>ndvi</code>: Normalized Difference Vegetation Index grid.</li>
<li>
<code>nepal</code>: Health, poverty and education indicators for Nepal districts (2007-14).</li>
<li>
<code>nyc</code>: Rental Housing and Demographics in NYC (2000s), non-spatial.</li>
<li>
<code>nyc_sf</code>: Rental Housing and Demographics in NYC (2000s).</li>
<li>
<code>nycearnings</code>: Block-level Earnings in NYC (2002-14).</li>
<li>
<code>nyceducation</code>: NYC Education (2000).</li>
<li>
<code>nycneighborhood</code>: Demographics for New York City neighborhoods (2000s).</li>
<li>
<code>nycsociodemo</code>: NYC Education and Socio-Demographics (2000s).</li>
<li>
<code>ohio_lung</code>: Ohio Lung Cancer Mortality (1960s-80s).</li>
<li>
<code>orlando1</code>: 2000 Census Tract Data for Orlando, FL MSA and counties.</li>
<li>
<code>orlando2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Orlando, FL MSA.</li>
<li>
<code>ozone9799</code>: Monthly ozone data, 1997-99.</li>
<li>
<code>phoenixACS</code>: Phoenix American Community Survey Data (2010, 5-year averages).</li>
<li>
<code>pittsburghom</code>: Pittsburgh homicide locations (1993).</li>
<li>
<code>sacramento1</code>: 2000 Census Tract Data for Sacramento MSA.</li>
<li>
<code>sacramento2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Sacramento MSA.</li>
<li>
<code>savannah1</code>: 2000 Census Tract Data for Savannah, GA MSA and counties.</li>
<li>
<code>savannah2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Savannah, GA MSA.</li>
<li>
<code>scotlip</code>: Male lip cancer in Scotland, 1975-80.</li>
<li>
<code>seattle1</code>: 2000 Census Tract Data for Seattle, WA MSA and counties.</li>
<li>
<code>seattle2</code>: 1998 and 2001 Zip Code Business Patterns (Census Bureau) for Seattle, WA MSA.</li>
<li>
<code>Sfcartheft</code>: Car theft incidents in San Francisco (points and area) - for CAST (July-Dec 2012).</li>
<li>
<code>Sfcrime</code>: Crime incidents in San Francisco (points and area) - for CAST (July-Dec 2012).</li>
<li>
<code>Sfdrugs</code>: Drug incidents in San Francisco (points and area) - for CAST (July-Dec 2012).</li>
<li>
<code>Sfrobbery</code>: Robbery incidents in San Francisco (points and area) - for CAST (July-Dec 2012).</li>
<li>
<code>Sfvandalism</code>: Vandalism incidents in San Francisco (points and area) - for CAST (July-Dec 2012).</li>
<li>
<code>SIDS</code>: North Carolina county SIDS death counts (1970s and 1980s).</li>
<li>
<code>snow</code>: John Snow &amp; the 19th Century Cholera Epidemic.</li>
<li>
<code>southom</code>: US Southern county homicides 1960-1990.</li>
<li>
<code>stlouishom</code>: St Louis region county homicide counts and rates (1980s and 1990s).</li>
<li>
<code>tampa</code>: 2000 Census Tract Data for Tampa, FL MSA and counties.</li>
<li>
<code>us_sdoh</code>: US Social Determinants of Health Data (2014).</li>
<li>
<code>vehicle_pts</code>: Point locations of abandoned vehicles in Chicago in September 2016.</li>
</ul>
</div>
<div id="similar-packages" class="section level2">
<h2 class="hasAnchor">
<a href="#similar-packages" class="anchor"></a>Similar Packages</h2>
<p>Interested in finding more R packages with spatial data included? Check out:</p>
<ul>
<li>
<a href="https://github.com/Nowosad/spData">spData</a>: package with large variety of spatial datasets, accompanies the <a href="https://geocompr.github.io">Geocomputation with R</a> book.</li>
<li>
<a href="https://github.com/walkerke/tidycensus">tidycensus</a> / <a href="https://github.com/walkerke/tigris">tigris</a>: API package for US Census data</li>
</ul>
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<li>Download from CRAN at <br><a href="https://cloud.r-project.org/package=geodaData">https://​cloud.r-project.org/​package=geodaData</a>
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<li>Browse source code at <br><a href="https://github.com/spatialanalysis/geodaData/">https://​github.com/​spatialanalysis/​geodaData/​</a>
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<li><NAME> <br><small class="roles"> Author, contributor </small> <a href="https://orcid.org/0000-0001-5103-1147X" target="orcid.widget" aria-label="ORCID"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a> </li>
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    <h1>Guerry "Moral Statistics" (1830s)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/guerry.R'><code>R/guerry.R</code></a></small>
    <div class="hidden name"><code>guerry.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Classic social science foundational study by <NAME> on crime, suicide, literacy and other “moral statistics” in 1830s France. Data from the R package Guerry (<NAME> and <NAME>).</p>
    </div>

    <pre class="usage"><span class='va'>guerry</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 85 rows, 23 variables, and a geometry column:</p><dl>
<dt>	dept	</dt><dd><p>Department ID: Standard numbers for the departments</p></dd>
<dt>	Region	</dt><dd><p>Region of France ('N'='North', 'S'='South', 'E'='East', 'W'='West', 'C'='Central').  Corsica is coded as NA.</p></dd>
<dt>	Dprtmnt	</dt><dd><p>Department name: Departments are named according to usage in 1830, but without accents. A factor with levels *Ain* *Aisne* *Allier* ... *<NAME>*</p></dd>
<dt>	Crm_prs	</dt><dd><p>Population per Crime against persons.</p></dd>
<dt>	Crm_prp	</dt><dd><p>Population per Crime against property.</p></dd>
<dt>	Litercy	</dt><dd><p>Percent of military conscripts who can read and write.</p></dd>
<dt>	Donatns	</dt><dd><p>Donations to the poor.</p></dd>
<dt>	Infants	</dt><dd><p>Population per illegitimate birth.</p></dd>
<dt>	Suicids	</dt><dd><p>Population per suicide.</p></dd>
<dt>	Maincty	</dt><dd><p>Size of principal city ('1:Sm', '2:Med', '3:Lg'), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium.</p></dd>
<dt>	Wealth	</dt><dd><p>Per capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant.</p></dd>
<dt>	Commerc	</dt><dd><p>Commerce and Industry, measured by the rank of the number of patents / population.</p></dd>
<dt>	Clergy	</dt><dd><p>Distribution of clergy, measured by the rank of the number of Catholic priests in active service population.</p></dd>
<dt>	Crim_prn	</dt><dd><p>Crimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830.</p></dd>
<dt>	Infntcd	</dt><dd><p>Infanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830.</p></dd>
<dt>	Dntn_cl	</dt><dd><p>Donations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824.</p></dd>
<dt>	Lottery	</dt><dd><p>Per capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826.</p></dd>
<dt>	Desertn	</dt><dd><p>Military desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827.</p></dd>
<dt>	Instrct	</dt><dd><p>Instruction. Ranks recorded from Guerry's map of Instruction. Note: this is inversely related to Literacy</p></dd>
<dt>	Prsttts	</dt><dd><p>Number of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth</p></dd>
<dt>	Distanc	</dt><dd><p>Distance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris)</p></dd>
<dt>	Area	</dt><dd><p>Area (1000 km^2).</p></dd>
<dt>	Pop1831	</dt><dd><p>Population in 1831, in 1000s</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    
<ul>
<li><p><NAME>. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour.</p></li>
<li><p><NAME>. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: <NAME> and <NAME>, Lewiston, N.Y. : Edwin Mellen Press, 2002.</p></li>
<li><p>Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36</p></li>
</ul><p><a href='https://geodacenter.github.io/data-and-lab/Guerry/'>https://geodacenter.github.io/data-and-lab/Guerry/</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, units in m. EPSG 27572: NTF (Paris) / Lambert zone II.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>guerry</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>guerry</span><span class='op'>[</span><span class='st'>"Donatns"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='guerry-1.png' alt='' width='700' height='433' /></div></pre>
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<title>Yield and Fertilizer in Las Rosas farm (Argentina) (1999 and 2001) — lasrosas • geodaData</title>


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<meta property="og:description" content="Yield data and Nitrogen fertilizer treatment with field characteristics for Las Rosas farm, Rio Cuarto, Cordoba, Argentina, 1999 and 2001. Use list2env(lasrosas, .GlobalEnv) to unlist in working environment. The data consist of a list of two objects:
lasrosas99 A spatial points object of class sf Dataframe with information from 1999.
lasrosas01 A spatial points object of class sf Dataframe with information from 2001.

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    <h1>Yield and Fertilizer in Las Rosas farm (Argentina) (1999 and 2001)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/lasrosas.R'><code>R/lasrosas.R</code></a></small>
    <div class="hidden name"><code>lasrosas.Rd</code></div>
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    <div class="ref-description">
    <p>Yield data and Nitrogen fertilizer treatment with field characteristics for Las Rosas farm, Rio Cuarto, Cordoba, Argentina, 1999 and 2001. Use <code><a href='https://rdrr.io/r/base/list2env.html'>list2env(lasrosas, .GlobalEnv)</a></code> to unlist in working environment. The data consist of a list of two objects:</p><ul>
<li><p>lasrosas99 A spatial points object of class sf Dataframe with information from 1999.</p></li>
<li><p>lasrosas01 A spatial points object of class sf Dataframe with information from 2001.</p></li>
</ul>

    </div>

    <pre class="usage"><span class='va'>lasrosas</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>lasrosas99 includes points with 1,738 rows, 34 variables, and a geometry column. lasrosas01 includes points with 1,705 rows, 26 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Identifier</p></dd>
<dt>	TOP2	</dt><dd><p>Topography dummy, Slope E</p></dd>
<dt>	TOP3	</dt><dd><p>Topography dummy, Hilltop</p></dd>
<dt>	TOP4	</dt><dd><p>Topography dummy, Slope W</p></dd>
<dt>	NXTOPz	</dt><dd><p>Interaction Nitrogen and topography zone (z: from 2 to 4)</p></dd>
<dt>	LONGITUDE	</dt><dd><p>Longitude</p></dd>
<dt>	LATITUDE	</dt><dd><p>Latitude</p></dd>
<dt>	OBS	</dt><dd><p>Observation number</p></dd>
<dt>	YIELD	</dt><dd><p>Corn yield (quintals per hectare)</p></dd>
<dt>	N	</dt><dd><p>Nitrogen fertilizer application (kg per hectare)</p></dd>
<dt>	N2	</dt><dd><p>Nitrogen squared</p></dd>
<dt>	TOPO	</dt><dd><p>Zone: Low E (1), Slope E (2), Hilltop (3), Slope W (4)</p></dd>
<dt>	BV	</dt><dd><p>Brightness value (proxy for low organic matter content)</p></dd>
<dt>	BV2	</dt><dd><p>Brightness squared</p></dd>
<dt>	NXBV	</dt><dd><p>Interaction Nitrogen - brightness</p></dd>
<dt>	BVXT2z	</dt><dd><p>Interaction brightness and topographic zone (z: from 2 to 4)</p></dd>
<dt>	BV2XTz 	</dt><dd><p>Interaction squared brightness and topographic zone (z: from 2 to 4)</p></dd>
<dt>	SAT (99)	</dt><dd><p>Red to NIR ratio (proxy for low organic matter content)</p></dd>
<dt>	SAT2 (99)	</dt><dd><p>SAT squared</p></dd>
<dt>	NXSAT (99)	</dt><dd><p>Interaction Nitrogen - Red to NIR ratio</p></dd>
<dt>	SATXTz (99)	</dt><dd><p>Interaction Sat - topographic zone (z: from 2 to 4)</p></dd>
<dt>	SAT2XTz (99)	</dt><dd><p>Interaction Sat squared - topographic zone (z: from 2 to 4)</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p><NAME>, Instituto Nacional de Tecnologia Agropecuaria (INTA), Manfredi, Cordoba, Argentina, (<EMAIL>)</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>List of two Sf objects, unprojected. The CRS of lasrosas99 is EPSG 4326: WGS84. The data lasrosas99 has an undefined Coordinate Reference System.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"></pre>
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<meta property="og:description" content="2000 Census Tract Data for Charleston, SC MSA and counties." />




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    <h1>Charleston 2000 Employment</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/charleston1.R'><code>R/charleston1.R</code></a></small>
    <div class="hidden name"><code>charleston1.Rd</code></div>
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    <div class="ref-description">
    <p>2000 Census Tract Data for Charleston, SC MSA and counties.</p>
    </div>

    <pre class="usage"><span class='va'>charleston1</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 117 rows, 31 variables, and a geometry column:</p><dl>
<dt>	FIPS	</dt><dd><p>FIPS Code</p></dd>
<dt>	msa	</dt><dd><p>MSA Name</p></dd>
<dt>	tot_pop	</dt><dd><p>Total population: Total</p></dd>
<dt>	pop_16	</dt><dd><p>Total population: Under 16</p></dd>
<dt>	pop_65	</dt><dd><p>Total population: 65+</p></dd>
<dt>	white#	</dt><dd><p>Total population: Not Hispanic or Latino; White alone</p></dd>
<dt>	black#	</dt><dd><p>Total population: Not Hispanic or Latino; Black or African American alone</p></dd>
<dt>	asian#	</dt><dd><p>Total population: Not Hispanic or Latino; Asian alone</p></dd>
<dt>	hisp#	</dt><dd><p>Total population: Hispanic or Latino</p></dd>
<dt>	multi_ra	</dt><dd><p>Total population: Not Hispanic or Latino; Two or more races</p></dd>
<dt>	males	</dt><dd><p>Total population: Male</p></dd>
<dt>	females	</dt><dd><p>Total population: Female</p></dd>
<dt>	male1664	</dt><dd><p>Total population: Male working age 16-64</p></dd>
<dt>	fem1664	</dt><dd><p>Total population: Female working age 16-64</p></dd>
<dt>	empl16	</dt><dd><p>Workers 16 years and over: Total</p></dd>
<dt>	emp_away	</dt><dd><p>Workers 16 years and over: Did not work at home</p></dd>
<dt>	emp_home	</dt><dd><p>Workers 16 years and over: Worked at home</p></dd>
<dt>	emp_29	</dt><dd><p>Workers 16 years and over: Did not work at home; Travel time to work; Less than 30 minutes</p></dd>
<dt>	emp_30	</dt><dd><p>Workers 16 years and over: Did not work at home; Travel time to work; 30 minutes or more</p></dd>
<dt>	emp16_2	</dt><dd><p>Employed civilian population 16 years and over: Total</p></dd>
<dt>	emp_male	</dt><dd><p>Employed civilian population 16 years and over: Male</p></dd>
<dt>	emp_fem	</dt><dd><p>Employed civilian population 16 years and over: Female</p></dd>
<dt>	occ_man	</dt><dd><p>Employed civilian population 16 years and over: Production occupations</p></dd>
<dt>	occ_off1	</dt><dd><p>Employed civilian population 16 years and over: Office (001-219 except 020, 021)</p></dd>
<dt>	occ_info	</dt><dd><p>Employed civilian population 16 years and over: Computer and mathematical occupations</p></dd>
<dt>	hh_inc	</dt><dd><p>Households: Median household income in 1999</p></dd>
<dt>	pov_pop*	</dt><dd><p>Population for whom poverty status is determined: Total Population</p></dd>
<dt>	pov_tot*	</dt><dd><p>Population for whom poverty status is determined: Income in 1999 below poverty level</p></dd>
<dt>	hsg_val	</dt><dd><p>Owner-occupied housing units: Median value</p></dd>
<dt>	polyid	</dt><dd><p>Unique ID</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>2000 Census, Summary File 3. Available at <a href='http://factfinder.census.gov'>http://factfinder.census.gov</a>. Note that the poverty variable names can be easily confused: pov_pop is the reference population (i.e. total population); pov_tot is all poor persons.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>charleston1</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>charleston1</span><span class='op'>[</span><span class='st'>"EMP_MALE"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='charleston1-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>Boston Housing 1970s</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/bostonhsg.R'><code>R/bostonhsg.R</code></a></small>
    <div class="hidden name"><code>bostonhsg.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Housing and neighborhood data for the city of Boston based on research from the 1970s-90s.</p>
    </div>

    <pre class="usage"><span class='va'>bostonhsg</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 506 rows, 24 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Sequential ID</p></dd>
<dt>	TOWN	</dt><dd><p>A factor with levels given by town names</p></dd>
<dt>	TOWNNO	</dt><dd><p>A numeric vector corresponding to TOWN</p></dd>
<dt>	TRACT	</dt><dd><p>A numeric vector of tract ID numbers</p></dd>
<dt>	LON	</dt><dd><p>A numeric vector of tract point longitudes in decimal degrees</p></dd>
<dt>	LAT	</dt><dd><p>A numeric vector of tract point latitudes in decimal degrees</p></dd>
<dt>	X	</dt><dd><p>X Coordinates (UTM Zone 19)</p></dd>
<dt>	Y	</dt><dd><p>Y Coordinates (UTM Zone 19)</p></dd>
<dt>	MEDV	</dt><dd><p>A numeric vector of median values of owner-occupied housing in USD 1000</p></dd>
<dt>	CMEDV	</dt><dd><p>A numeric vector of corrected median values of owner-occupied housing in USD 1000</p></dd>
<dt>	CRIM	</dt><dd><p>A numeric vector of per capita crime</p></dd>
<dt>	ZN	</dt><dd><p>A numeric vector of proportions of residential land zoned for lots over 25000 sq. ft per town (constant for all Boston tracts)</p></dd>
<dt>	INDUS	</dt><dd><p>A numeric vector of proportions of non-retail business acres per town (constant for all Boston tracts)</p></dd>
<dt>	CHAS	</dt><dd><p>A factor with levels 1 if tract borders Charles River; 0 otherwise</p></dd>
<dt>	NOX	</dt><dd><p>A numeric vector of nitric oxides concentration (parts per 10 million) per town</p></dd>
<dt>	RM	</dt><dd><p>A numeric vector of average numbers of rooms per dwelling</p></dd>
<dt>	AGE	</dt><dd><p>A numeric vector of proportions of owner-occupied units built prior to 1940</p></dd>
<dt>	DIS	</dt><dd><p>A numeric vector of weighted distances to five Boston employment centers</p></dd>
<dt>	RAD	</dt><dd><p>A numeric vector of an index of accessibility to radial highways per town (constant for all Boston tracts)</p></dd>
<dt>	TAX	</dt><dd><p>A numeric vector full-value property-tax rate per USD 10,000 per town (constant for all Boston tracts)</p></dd>
<dt>	PTRATIO	</dt><dd><p>A numeric vector of pupil-teacher ratios per town (constant for all Boston tracts)</p></dd>
<dt>	B	</dt><dd><p>A numeric vector of 1000*(Bk - 0.63)^2 where Bk is the proportion of blacks</p></dd>
<dt>	LSTAT	</dt><dd><p>A numeric vector of percentage values of lower status population</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p><NAME>, and <NAME>. (1978). Hedonic Housing Prices and the Demand for Clean Air, Journal of Environmental Economics and Management, Volume 5, 81- 102. Original data. <NAME>., and <NAME>. (1996). On the Harrison and Rubinfeld Data, Journal of Environmental Economics and Management, 31, 403-405. Provided corrections and examined censoring. Pace, <NAME>, and <NAME>. (1997). Using the Spatial Configuration of the Data to Improve Estimation, Journal of the Real Estate Finance and Economics, 14, 333-340.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, undefined Coordinate Reference System.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>bostonhsg</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>bostonhsg</span><span class='op'>[</span><span class='st'>"RM"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='bostonhsg-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>NYC Education and Sociodemographics</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/nycsociodemo.R'><code>R/nycsociodemo.R</code></a></small>
    <div class="hidden name"><code>nycsociodemo.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Education and socio-demographic characteristics for New York City Census tracts.</p>
    </div>

    <pre class="usage"><span class='va'>nycsociodemo</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 2,166 rows, 114 variables, and a geometry column:</p><dl>
 <dt>	carto_id	</dt><dd><p>Carto unique ID</p></dd>
<dt>	withssi	</dt><dd><p>Households With Supplemental Security Income</p></dd>
<dt>	withpubass	</dt><dd><p>Households With Public Assistance Income</p></dd>
<dt>	struggling	</dt><dd><p>Struggling as regards Ratio Of Income In 2012 To Poverty Level (1.00 to 1.99)</p></dd>
<dt>	profession	</dt><dd><p>Population 25 Years and over with educational attainment of a professional degree level and below</p></dd>
<dt>	popunemplo	</dt><dd><p>Unemployed total population</p></dd>
<dt>	poptot	</dt><dd><p>Total Population</p></dd>
<dt>	popover18	</dt><dd><p>Total Population Over 18 years</p></dd>
<dt>	popinlabou	</dt><dd><p>Total population in labour force</p></dd>
<dt>	poororstru	</dt><dd><p>Poor or struggling as regards Ratio Of Income In 2012 To Poverty Level (Under 2.00)</p></dd>
<dt>	poor	</dt><dd><p>Doing poorly as regard Ratio Of Income In 2012 To Poverty Level (Under 1.00)</p></dd>
<dt>	pacificune	</dt><dd><p>Hawaiian &amp; Pacific Islander unemployed population</p></dd>
<dt>	pacificinl	</dt><dd><p>Hawaiian &amp; Pacific Islanders in labour population</p></dd>
<dt>	pacific	</dt><dd><p>Total Population Pacific Islander</p></dd>
<dt>	otherunemp	</dt><dd><p>Other ethnic unemployed population</p></dd>
<dt>	otherinlab	</dt><dd><p>Other ethnic in labour force</p></dd>
<dt>	otherethni	</dt><dd><p>Total Population Other Race</p></dd>
<dt>	onlyprofes	</dt><dd><p>Population 25 Years and over with educational attainment of a professional degree level and below</p></dd>
<dt>	onlymaster	</dt><dd><p>Population 25 Years and over with educational attainment of a masters level degree and below</p></dd>
<dt>	onlylessth	</dt><dd><p>Population 25 Years and over with educational attainment of only less than high school</p></dd>
<dt>	onlyhighsc	</dt><dd><p>Population 25 Years and over with educational attainment of only high school level</p></dd>
<dt>	onlydoctor	</dt><dd><p>Population 25 Years and over with educational attainment of doctorate level degree and below</p></dd>
<dt>	onlycolleg	</dt><dd><p>Population 25 Years and over with educational attainment of college level and below</p></dd>
<dt>	onlybachel	</dt><dd><p>Population 25 Years and over with educational attainment of a bachelors level degree and below</p></dd>
<dt>	okay	</dt><dd><p>Doing okay as regards Ratio Of Income In 2012 To Poverty Level (2.00 and over)</p></dd>
<dt>	mixedunemp	</dt><dd><p>Mixed ethnic unemployed population</p></dd>
<dt>	mixedinlab	</dt><dd><p>Mixed ethnic people in labour force</p></dd>
<dt>	mixed	</dt><dd><p>Total Population Mixed race</p></dd>
<dt>	master	</dt><dd><p>Population 25 Years and over with educational attainment of at least a masters degree</p></dd>
<dt>	maleunempl	</dt><dd><p>Unemployed male population</p></dd>
<dt>	maleover18	</dt><dd><p>Total Population Male Over 18 years</p></dd>
<dt>	malepro	</dt><dd><p>Male Population 25 Years and over with educational attainment of a professional degree level and below</p></dd>
<dt>	malemastr	</dt><dd><p>Male Population 25 Years and over with educational attainment of a masters level degree and below</p></dd>
<dt>	male_lesHS	</dt><dd><p>Male Population 25 Years and over with educational attainment of only less than high school</p></dd>
<dt>	male_HS	</dt><dd><p>Male Population 25 Years and over with educational attainment of only high school level</p></dd>
<dt>	male_doctr	</dt><dd><p>Male Population 25 Years and over with educational attainment of doctorate level degree and below</p></dd>
<dt>	male_collg	</dt><dd><p>Male Population 25 Years and over with educational attainment of college level and below</p></dd>
<dt>	male_BA	</dt><dd><p>Male Population 25 Years and over with educational attainment of a bachelors level degree and below</p></dd>
<dt>	maleinlabo	</dt><dd><p>Male population in labour force</p></dd>
<dt>	maledrop	</dt><dd><p>Male Not high school graduate, not enrolled (dropped out) - 16-19 age band</p></dd>
<dt>	male16to19	</dt><dd><p>Male Civilian Population 16 To 19 Years</p></dd>
<dt>	male	</dt><dd><p>Total Population Male</p></dd>
<dt>	lessthanhi	</dt><dd><p>Population 25 Years and over with educational attainment of at least less than high school</p></dd>
<dt>	lessthan10	</dt><dd><p>Household Income less than 10000</p></dd>
<dt>	households	</dt><dd><p>Total Households</p></dd>
<dt>	hispanicun	</dt><dd><p>Hispanic unemployed population</p></dd>
<dt>	hispanicin	</dt><dd><p>Hispanics in Labour force</p></dd>
<dt>	hispanic	</dt><dd><p>Total Population Hispanic</p></dd>
<dt>	highschool	</dt><dd><p>Population 25 Years and over with educational attainment of at least high school level</p></dd>
<dt>	femaleunem	</dt><dd><p>Unemployed female population</p></dd>
<dt>	femaleover	</dt><dd><p>Total Population Female Over 18 years</p></dd>
<dt>	fem_profes	</dt><dd><p>Female Population 25 Years and over with educational attainment of a professional degree level and below</p></dd>
<dt>	fem_master	</dt><dd><p>Female Population 25 Years and over with educational attainment of a masters level degree and below</p></dd>
<dt>	fem_lessHS	</dt><dd><p>Female Population 25 Years and over with educational attainment of only less than high school</p></dd>
<dt>	fem_HS	</dt><dd><p>Female Population 25 Years and over with educational attainment of only high school level</p></dd>
<dt>	fem_doctor	</dt><dd><p>Female Population 25 Years and over with educational attainment of doctorate level degree and below</p></dd>
<dt>	fem_colleg	</dt><dd><p>Female Population 25 Years and over with educational attainment of college level and below</p></dd>
<dt>	fem_BA	</dt><dd><p>Female Population 25 Years and over with educational attainment of a bachelors level degree and below</p></dd>
<dt>	femaleinla	</dt><dd><p>Female population in labour force</p></dd>
<dt>	femaledrop	</dt><dd><p>Female Not high school graduate, not enrolled (dropped out) - 16-19 age band</p></dd>
<dt>	femal16_19	</dt><dd><p>Female Civilian Population 16 To 19 Years</p></dd>
<dt>	female	</dt><dd><p>Total Population Female</p></dd>
<dt>	europeanun	</dt><dd><p>European American unemployed population</p></dd>
<dt>	europeanin	</dt><dd><p>European Americans in labour force</p></dd>
<dt>	european	</dt><dd><p>Total Population White</p></dd>
<dt>	doctorate	</dt><dd><p>Population 25 Years and over with educational attainment of at least doctorate level</p></dd>
<dt>	com_90plus	</dt><dd><p>More than 90 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	comm_less5	</dt><dd><p>Less than 5 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	comm_60_89	</dt><dd><p>60 to 89 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	comm_5_14	</dt><dd><p>5 to 14 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	comm_45_59	</dt><dd><p>45 to 59 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	comm_30_44	</dt><dd><p>30 to 44 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	comm_15_29	</dt><dd><p>15 to 29 min commute to work for Workers 16 Years And Over Who Did Not Work At Home</p></dd>
<dt>	college	</dt><dd><p>Population 25 Years and over with educational attainment of at least college level</p></dd>
<dt>	bachelor	</dt><dd><p>Population 25 Years and over with educational attainment of at least a bachelors degree</p></dd>
<dt>	asianunemp	</dt><dd><p>Asian American unemployed population</p></dd>
<dt>	asianinlab	</dt><dd><p>Asian Americans in labour force</p></dd>
<dt>	asian	</dt><dd><p>Total Population Asian American</p></dd>
<dt>	americanun	</dt><dd><p>American Indian unemployed population</p></dd>
<dt>	americanin	</dt><dd><p>American Indians in labour force</p></dd>
<dt>	american	</dt><dd><p>Total Population American Indian</p></dd>
<dt>	africanune	</dt><dd><p>African American unemployed population</p></dd>
<dt>	africaninl	</dt><dd><p>African Americans in labour force</p></dd>
<dt>	african	</dt><dd><p>Total Population African American</p></dd>
<dt>	ntaname	</dt><dd><p>New York City NTA (Neighborhood Tabulation Area) name</p></dd>
<dt>	ntacode	</dt><dd><p>code associatd with the NTA</p></dd>
<dt>	boroname	</dt><dd><p>Name for each of the five New York City boroughs</p></dd>
<dt>	medianinco	</dt><dd><p>Median household income (In 2012 Inflation Adjusted Dollars)</p></dd>
<dt>	medianagem	</dt><dd><p>Total Population Male Median Age</p></dd>
<dt>	medianagef	</dt><dd><p>Total Population Female Median Age</p></dd>
<dt>	medianage	</dt><dd><p>Total Population Median Age</p></dd>
<dt>	gini	</dt><dd><p>Gini Index Of Income Inequality</p></dd>
<dt>	borocode	</dt><dd><p>Number for each of the five New York City boroughs</p></dd>
<dt>	popdty	</dt><dd><p>Population Density (per sq. mile)</p></dd>
<dt>	uemprate	</dt><dd><p>Unemployment rate</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>American Community Survey 2008-2012, US Census Bureau.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, undefined Coordinate Reference System.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>nycsociodemo</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>nycsociodemo</span><span class='op'>[</span><span class='st'>"bachelor"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='nycsociodemo-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>Chicago Health and Socio-Economics</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/chisociohealth.R'><code>R/chisociohealth.R</code></a></small>
    <div class="hidden name"><code>chisociohealth.Rd</code></div>
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    <div class="ref-description">
    <p>Public health and socio-economic indicators for the 77 community areas of Chicago, IL, 2014.</p>
    </div>

    <pre class="usage"><span class='va'>chisociohealth</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 77 rows, 86 variables, and a geometry column:</p><dl>
<dt>ComAreaID</dt><dd><p>Community Area ID</p></dd>
<dt>community</dt><dd><p>Community Area Name</p></dd>
<dt>TRACTnt</dt><dd><p>Tract</p></dd>
<dt>shape_area</dt><dd><p>polygon area</p></dd>
<dt>shape_len</dt><dd><p>polygon perimeter</p></dd>
<dt>Pop2012</dt><dd><p>Population in 2012</p></dd>
<dt>Pop2014</dt><dd><p>Population in 2014</p></dd>
<dt>PopChng</dt><dd><p>Population change</p></dd>
<dt>PopM</dt><dd><p>Male Population in 2014</p></dd>
<dt>PopF</dt><dd><p>Female population in 2014</p></dd>
<dt>Under5</dt><dd><p>Population age 0-5 in 2014</p></dd>
<dt>Under18</dt><dd><p>Population age 0-18 in 2014</p></dd>
<dt>Over21</dt><dd><p>Population over age 21 in 2014</p></dd>
<dt>Over65</dt><dd><p>Population over age 65 in 2014</p></dd>
<dt>Wht14</dt><dd><p>White population in 2014</p></dd>
<dt>Blk14</dt><dd><p>Black population in 2014</p></dd>
<dt>AI14</dt><dd><p>American Indian population in 2014</p></dd>
<dt>AS14</dt><dd><p>Asian population in 2014</p></dd>
<dt>NHP14</dt><dd><p>Native Hawaiian population in 2014</p></dd>
<dt>Oth14</dt><dd><p>Population of other races in 2014</p></dd>
<dt>Hisp14</dt><dd><p>Hispanic population in 2014</p></dd>
<dt>PopMP</dt><dd><p>Proportion of male in the overall population in 2014</p></dd>
 <dt>PopFP</dt><dd><p>Proportion of female in the overall population in 2014</p></dd>
 <dt>Under5P</dt><dd><p>Proportion of under 5 in the overall population in 2014</p></dd>
 <dt>Under18P</dt><dd><p>Proportion of under 18 in the overall population in 2014</p></dd>
 <dt>Over18P</dt><dd><p>Proportion of over 18 in the overall population in 2014</p></dd>
 <dt>Over21P</dt><dd><p>Proportion of over 21 in the overall population in 2014</p></dd>
 <dt>Over65P</dt><dd><p>Proportion of over 65 in the overall population in 2014</p></dd>
 <dt>Wht14P</dt><dd><p>Proportion of white population in 2014</p></dd>
 <dt>Blk14P</dt><dd><p>Proportion of black population in 2014</p></dd>
 <dt>AI14P</dt><dd><p>Proportion of American Indian population in 2014</p></dd>
 <dt>AS14P</dt><dd><p>Proportion of asian population in 2014</p></dd>
 <dt>NHP14P</dt><dd><p>Proportion of native hawaiian population  in 2014</p></dd>
 <dt>Oth14P</dt><dd><p>Proportion of other races population  in 2014</p></dd>
 <dt>Hisp14P</dt><dd><p>Proportion of hispanic population  in 2014</p></dd>
<dt>Property_C</dt><dd><p>Number of property crimes</p></dd>
<dt>PropCrRt</dt><dd><p>Property crime rate in 2014</p></dd>
<dt>Violent_C</dt><dd><p>Number of violent crimes</p></dd>
<dt>VlntCrRt</dt><dd><p>Violent crime rate in 2014</p></dd>
<dt>PerCInc14</dt><dd><p>Per Capita Income</p></dd>
<dt>PPop14</dt><dd><p>2014 Population base for 2014 poverty counts</p></dd>
<dt>Pov14</dt><dd><p>Number of people living in poverty in 2014</p></dd>
<dt>ChildPov14</dt><dd><p>Number of children (age 0-18) living in poverty in 2014</p></dd>
<dt>NoHS14</dt><dd><p>Number of adults without a high school diploma in 2014</p></dd>
<dt>HSGrad14</dt><dd><p>Number of high school graduates in 2014</p></dd>
<dt>SmClg14</dt><dd><p>Number of people who have some college education in 2014</p></dd>
<dt>ClgGrad14</dt><dd><p>Number of people who have a college degree in 2014</p></dd>
<dt>LaborFrc</dt><dd><p>Number of people in the labor force in 2014</p></dd>
<dt>Unemp14</dt><dd><p>Number of people unemployed in 2014</p></dd>
<dt>Pov50</dt><dd><p>Number of people with income below 50pct of the poverty line</p></dd>
<dt>Pov50P</dt><dd><p>Proportion of people with income below 50pct of the poverty line</p></dd>
<dt>Pov125</dt><dd><p>Number of people with income below 125pct of the poverty line</p></dd>
<dt>Pov125P</dt><dd><p>Proportion of people with income below 125pct of the poverty line</p></dd>
<dt>Pov150</dt><dd><p>Number of people with income below 150pct of the poverty line</p></dd>
<dt>Pov150P</dt><dd><p>Proportion of people with income below 150pct of the poverty line</p></dd>
<dt>Pov185</dt><dd><p>Number of people with income below 185pct of the poverty line</p></dd>
<dt>Pov185P</dt><dd><p>Proportion of people with income below 185pct of the poverty line</p></dd>
<dt>Pov200</dt><dd><p>Number of people with income below 200pct of the poverty line</p></dd>
<dt>Pov200P</dt><dd><p>Number of people with income below 200pct of the poverty line</p></dd>
<dt>SESave</dt><dd><p>Average SES</p></dd>
<dt>COIave</dt><dd><p>Average Childhood Opportunity Index</p></dd>
<dt>HISave</dt><dd><p>Average Economic Hardship Index</p></dd>
<dt>Hlitave</dt><dd><p>Average health literacy</p></dd>
<dt>Birthrate</dt><dd><p>Birth rate per 1000 residents</p></dd>
<dt>FertRate</dt><dd><p>Fertility rate per 1000 females aged 15-44</p></dd>
<dt>LowBirthR</dt><dd><p>Percent of live births with low birth weight</p></dd>
<dt>PrenScrn</dt><dd><p>Percent of females delivering a live birth, in which prenatal care began in first trimester</p></dd>
<dt>PretBrth</dt><dd><p>Preterm births as percent of live births</p></dd>
<dt>TeenBirth</dt><dd><p>Teen birth rate per 1000 females aged 15-19</p></dd>
<dt>Assault</dt><dd><p>Homicide deaths per 100,000 persons (age adjusted)</p></dd>
<dt>BrstCancr</dt><dd><p>Breast cancer deaths per 100,000 females (age adjusted)</p></dd>
<dt>CancerAll</dt><dd><p>Cancer deaths per 100,000 persons (age adjusted)</p></dd>
<dt>Colorect</dt><dd><p>Colorectal cancer deaths per 100,000 persons (age adjusted)</p></dd>
<dt>DiabetM</dt><dd><p>Diabetes-related deaths per 100,000 persons (age adjusted)</p></dd>
<dt>FirearmM</dt><dd><p>Firearm-related deaths per 100,000 persons (age adjusted)</p></dd>
<dt>InfntMR</dt><dd><p>Infant mortality rate: deaths per 1000 live births</p></dd>
<dt>LungCancer</dt><dd><p>Lung cancer deaths per 100,000 persons (age adjusted)</p></dd>
<dt>ProstateC</dt><dd><p>Prostate cancer deaths per 100,000 males (age adjusted)</p></dd>
<dt>Stroke</dt><dd><p>Stroke deaths per 100,000 persons (age adjusted)</p></dd>
<dt>ChlBLLS</dt><dd><p>Childhood blood lead level screening, per 1000 children aged 0-6 years</p></dd>
<dt>ChlLeadP</dt><dd><p>Childhood lead poisoning per 100</p></dd>
<dt>GonorrF</dt><dd><p>Gonorrhea in females, per 10,000 females age 15-44</p></dd>
<dt>GonorrM</dt><dd><p>Gonorrhea in males, per 10,000 females age 15-44</p></dd>
<dt>Tuberc</dt><dd><p>Tuberculosis per 100,000 persons</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>Economic Hardship Index method based on the Intercity Hardship Index, by <NAME> and <NAME>, Jr. in Understanding Urban Hardship, Political Science Quarterly 91 (Spring 1976): 47-62.). Main source: <a href='https://data.cityofchicago.org/Health-Human-Services/Public-Health-Statistics-Selected-public-health-in/iqnk-2tcu'>https://data.cityofchicago.org/Health-Human-Services/Public-Health-Statistics-Selected-public-health-in/iqnk-2tcu</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>chisociohealth</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>chisociohealth</span><span class='op'>[</span><span class='st'>"Under5"</span><span class='op'>]</span><span class='op'>)</span>
 <span class='op'>}</span>
</div><div class='img'><img src='chisociohealth-1.png' alt='' width='700' height='433' /></div></pre>
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<meta property="og:title" content="<NAME> &amp; the 19th Century Cholera Epidemic — snow" />
<meta property="og:description" content="Datasets about the Broad St Pump and South London Natural Experiment cases in London (Snow, 1855). Use list2env(snow, .GlobalEnv) to unlist in working environment. The data consist of a list of six objects:
snow2 A shapefile object of class sf Dataframe with spatial points of buildings where cholera deaths were recorded near the Broad Street pump.
snow3 A shapefile object of class sf Dataframe with polygons of housing blocks, in the forms of polygons, which aggregate cholera deaths in the vicinity of the Broad Street pump.
snow4 A shapefile object of class sf Dataframe with spatial points that represent the Broad Street pump and the five pumps closest to it.
snow5 A shapefile object of class sf Dataframe with rings of individual cholera deaths and the Broad Street pump, extracted from snow4.
snow6 A shapefile object of class sf Dataframe with rings of individual cholera deaths and the five pumps except for the Broad Street pump, extracted from the 6 pumps dataset (from snow4).
snow7 A shapefile object of class sf Dataframe with polygons that correspond to the South London Natural Experiment attribute data compiled by Coleman (2019).

" />




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    <h1><NAME> &amp; the 19th Century Cholera Epidemic</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/snow.R'><code>R/snow.R</code></a></small>
    <div class="hidden name"><code>snow.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Datasets about the Broad St Pump and South London Natural Experiment cases in London (Snow, 1855). Use <code><a href='https://rdrr.io/r/base/list2env.html'>list2env(snow, .GlobalEnv)</a></code> to unlist in working environment. The data consist of a list of six objects:</p><ul>
<li><p>snow2 A shapefile object of class sf Dataframe with spatial points of buildings where cholera deaths were recorded near the Broad Street pump.</p></li>
<li><p>snow3 A shapefile object of class sf Dataframe with polygons of housing blocks, in the forms of polygons, which aggregate cholera deaths in the vicinity of the Broad Street pump.</p></li>
<li><p>snow4 A shapefile object of class sf Dataframe with spatial points that represent the Broad Street pump and the five pumps closest to it.</p></li>
<li><p>snow5 A shapefile object of class sf Dataframe with rings of individual cholera deaths and the Broad Street pump, extracted from snow4.</p></li>
<li><p>snow6 A shapefile object of class sf Dataframe with rings of individual cholera deaths and the five pumps except for the Broad Street pump, extracted from the 6 pumps dataset (from snow4).</p></li>
<li><p>snow7 A shapefile object of class sf Dataframe with polygons that correspond to the South London Natural Experiment attribute data compiled by Coleman (2019).</p></li>
</ul>

    </div>

    <pre class="usage"><span class='va'>snow</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p><strong>snow2</strong> includes 250 rows, 8 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Unique ID</p></dd>
<dt>	x	</dt><dd><p>X coordinates (in meters)</p></dd>
<dt>	y	</dt><dd><p>Y coordinates (in meters)</p></dd>
<dt>	deaths	</dt><dd><p>Number of deaths per building</p></dd>
<dt>	pumpID	</dt><dd><p>ID of the nearest pump (see ‘pumps’ dataset)</p></dd>
<dt>	distpump	</dt><dd><p>Distance to the nearest pump (in meters - see ‘pumps’ dataset)</p></dd>
<dt>	distBSpump	</dt><dd><p>Distance to Broad St pump (in meters - see ‘pumps’ dataset)</p></dd>
<dt>	BSpump	</dt><dd><p>Create categories depending on whether the Broad Street pump is closest (1) or not (0)</p></dd>
<dt>	geometry	</dt><dd><p>Point</p></dd>
 
</dl>

<p><strong>snow3</strong> includes 40 rows, 3 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Unique ID</p></dd>
<dt>	deaths	</dt><dd><p>Number of deaths per polygon</p></dd>
<dt>	deathdens	</dt><dd><p>Number of deaths per polygon divided by population</p></dd>
<dt>	geometry	</dt><dd><p>Polygon</p></dd>
 
</dl>

<p><strong>snow4</strong> includes 6 rows, 4 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Pump ID</p></dd>
<dt>	x	</dt><dd><p>X coordinates (in meters)</p></dd>
<dt>	y	</dt><dd><p>Y coordinates (in meters)</p></dd>
<dt>	name	</dt><dd><p>Name of the pump</p></dd>
<dt>	geometry	</dt><dd><p>Point</p></dd>
 
</dl>

<p><strong>snow5</strong> includes 60 rows, 4 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Unique ID</p></dd>
<dt>	area	</dt><dd><p>Area (in squared meters)</p></dd>
<dt>	deaths	</dt><dd><p>Number of deaths per building</p></dd>
<dt>	deathdens	</dt><dd><p>Number of deaths per ring divided by area</p></dd>
<dt>	geometry	</dt><dd><p>Polygon</p></dd>
 
</dl>

<p><strong>snow6</strong> includes 35 rows, 6 variables, and a geometry column:</p><dl>
<dt>	ID	</dt><dd><p>Pump ID</p></dd>
<dt>	x	</dt><dd><p>X coordinates (in meters)</p></dd>
<dt>	y	</dt><dd><p>Y coordinates (in meters)</p></dd>
<dt>	dist	</dt><dd><p>Distance to pump (in meters)</p></dd>
<dt>	deaths	</dt><dd><p>Number of deaths per ring</p></dd>
<dt>	geometry	</dt><dd><p>Polygon</p></dd>
 
</dl>

<p><strong>snow7</strong> includes 32 rows, 32 variables, and a geometry column:</p><dl>
<dt>	dis_ID	</dt><dd><p>London district ID</p></dd>
<dt>	district	</dt><dd><p>London district</p></dd>
<dt>	sub_ID	</dt><dd><p>London subdistrict ID</p></dd>
<dt>	subdist	</dt><dd><p>London subdistrict</p></dd>
<dt>	pop1851	</dt><dd><p>Population for 1851</p></dd>
<dt>	supplier	</dt><dd><p>Water company suppliers that served the subdistrict</p></dd>
<dt>	supplierID	</dt><dd><p>Water company supplier ID</p></dd>
<dt>	perc_sou	</dt><dd><p>Proportion of the population that was served by the Southwark &amp; Vauxhall company</p></dd>
<dt>	perc_lam	</dt><dd><p>Proportion of the population that was served by the Lambeth company</p></dd>
<dt>	perc_other	</dt><dd><p>Proportion of the population that was served by a company other than Southwark &amp; Vauxhall or Lambeth</p></dd>
<dt>	lam_degree	</dt><dd><p>Creates categories for the proportion of the population that was served by the Lambeth company</p></dd>
<dt>	d_overall	</dt><dd><p>Number of deaths attributed to the cholera epidemic in the seven weeks ending August 26, 1854</p></dd>
<dt>	d_sou	</dt><dd><p>Number of deaths attributed to the cholera epidemic in the seven weeks ending August 26, 1854 for the Southwark &amp; Vauxhall company</p></dd>
<dt>	d_lam	</dt><dd><p>Number of deaths attributed to the cholera epidemic in the seven weeks ending August 26, 1854 for the Lambeth company</p></dd>
<dt>	d_pump	</dt><dd><p>Number of deaths attributed to the cholera epidemic in the seven weeks ending August 26, 1854 originating in pump-wells</p></dd>
<dt>	d_thames	</dt><dd><p>Number of deaths attributed to the cholera epidemic in the seven weeks ending August 26, 1854 from water from the Thames River and ditches</p></dd>
<dt>	rate_sou7w	</dt><dd><p>Southwark &amp; Vauxhall cholera death rate per 10000 people in the seven weeks ending August 26, 1854</p></dd>
<dt>	rate_lam7w	</dt><dd><p>Lambeth cholera death rate per 10000 people in the seven weeks ending August 26, 1854 - Missing values are undefined and should not be converted to 0</p></dd>
<dt>	rate_oth7w	</dt><dd><p>Cholera death rate per 10000 people for ‘other’ category in the seven weeks ending August 26, 1854 - Missing values are undefined and should not be converted to 0</p></dd>
<dt>	deaths1849	</dt><dd><p>Number of deaths attributed to the cholera epidemic in 1849</p></dd>
<dt>	deaths1854	</dt><dd><p>Number of deaths attributed to the cholera epidemic in 1854</p></dd>
<dt>	rate1849	</dt><dd><p>Cholera death rate per 10000 people in 1849</p></dd>
<dt>	rate1854	</dt><dd><p>Cholera death rate per 10000 people in 1854</p></dd>
<dt>	pop1849	</dt><dd><p>Population for 1849</p></dd>
<dt>	pop1854	</dt><dd><p>Population for 1854</p></dd>
<dt>	rAvSupR_49	</dt><dd><p>Average supplier-region-specific cholera mortality rate per 10000 people in 1849</p></dd>
<dt>	rAvSupR_54	</dt><dd><p>Average supplier-region-specific cholera mortality rate per 10000 people in 1854</p></dd>
<dt>	pred_Snow	</dt><dd><p>Snow’s cholera death count prediction (from his 1856 Table VI)</p></dd>
<dt>	pred_DiD49	</dt><dd><p>Cholera death count prediction from Difference-in-Difference regression analysis for 1849</p></dd>
<dt>	pred_DiD54	</dt><dd><p>Cholera death count prediction from Difference-in-Difference regression analysis for 1854</p></dd>
<dt>	geometry	</dt><dd><p>Polygon</p></dd>
 
</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>A detailed description of the datasets and their construction can be found at: <a href='https://geodacenter.github.io/data-and-lab/snow'>https://geodacenter.github.io/data-and-lab/snow</a>. The GeoData center also has available snow1, which contains 578 individual deaths during the 1854 cholera epidemic that were compiled by Tobler (1994). snow1 was excluded from this package given that it can be reproduced from R package HistData, version 0.8-6.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>List of six Sf objects, projected to OSGB 1936 / British National Grid.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"></pre>
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kc_zones A shapefile object of class sf Dataframe.

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    <h1>2014-15 Home Sales in King County, WA</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/kchomesale.R'><code>R/kchomesale.R</code></a></small>
    <div class="hidden name"><code>kchomesale.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Home sales prices and characteristics for Seattle and King County, WA (May 2014 - 2015). Use <code><a href='https://rdrr.io/r/base/list2env.html'>list2env(kchomesale, .GlobalEnv)</a></code> to unlist in working environment. The data consist of a list of two objects:</p><ul>
<li><p>kchomesales_points A spatial points object of class sf Dataframe.</p></li>
<li><p>kc_zones A shapefile object of class sf Dataframe.</p></li>
</ul>

    </div>

    <pre class="usage"><span class='va'>kchomesale</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>kchomesales_points includes homesales points with 21,613 rows, 21 variables, and a geometry column.</p><dl>
 <dt>	id	</dt><dd><p>ID code</p></dd>
<dt>	date	</dt><dd><p>date sold</p></dd>
<dt>	price	</dt><dd><p>sale price</p></dd>
<dt>	bedrooms	</dt><dd><p>number of bedrooms</p></dd>
<dt>	bathrooms	</dt><dd><p>number of bathrooms</p></dd>
<dt>	sqft_liv	</dt><dd><p>size of living area in square feet</p></dd>
<dt>	sqft_lot	</dt><dd><p>size of the lot in square feet</p></dd>
<dt>	floors	</dt><dd><p>number of floors</p></dd>
<dt>	waterfront	</dt><dd><p>‘1’ if the property has a waterfront, ‘0’ if not</p></dd>
<dt>	view	</dt><dd><p>An index from 0 to 4 of how good the view of the property was</p></dd>
<dt>	condition	</dt><dd><p>condition of the house, ranked from 1 to 5</p></dd>
<dt>	grade	</dt><dd><p>Classification by construction quality which refers to the types of materials used and the quality of workmanship. Buildings of better quality (higher grade) cost more to build per unit of measure and command higher value. Additional details in: <a href='https://info.kingcounty.gov/assessor/esales/Glossary.aspx?type=r'>https://info.kingcounty.gov/assessor/esales/Glossary.aspx?type=r</a></p></dd>
<dt>	sqft_above	</dt><dd><p>square feet above ground</p></dd>
<dt>	sqft_basmt	</dt><dd><p>square feet below ground</p></dd>
<dt>	yr_built	</dt><dd><p>year built</p></dd>
<dt>	yr_renov	</dt><dd><p>year renovated. ‘0’ if never renovated</p></dd>
<dt>	zipcode	</dt><dd><p>5 digit zip code</p></dd>
<dt>	lat</dt><dd><p>latitude</p></dd>
<dt>  long	</dt><dd><p>longitude</p></dd>
<dt>	squft_liv15	</dt><dd><p>average size of interior housing living space for the closest 15 houses, in square feet</p></dd>
<dt>	squft_lot15	</dt><dd><p>average size of land lost for the closest 15 houses, in square feet</p></dd>
<dt>	OBJECTID	</dt><dd><p>Polygon ID</p></dd>
<dt>	ZIP	</dt><dd><p>ZIP code as int</p></dd>
<dt>	ZIPCODE	</dt><dd><p>ZIP code as chr</p></dd>
<dt>	COUNTY	</dt><dd><p>County code</p></dd>
<dt>	Shape_leng	</dt><dd><p>Polygon length in meters</p></dd>
<dt>	Shape_Area	</dt><dd><p>Polygon area in meters</p></dd>

</dl>

<p>kc_zones includes polygons of zones for King County with 85 rows, 6 variables and a geometry column).</p><dl>
 <dt>	OBJECTID	</dt><dd><p>ID code</p></dd>
<dt>	ZIP	</dt><dd><p>Zip code</p></dd>
<dt>	COUNTY	</dt><dd><p>County code</p></dd>
<dt>	ZIP_TYPE	</dt><dd><p>Zip code type: standard or PO box</p></dd>
<dt>	Shape_Leng	</dt><dd><p>Polygon length</p></dd>
<dt>	Shape_Area	</dt><dd><p>Polygon area</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>King County Home sales obtained from <a href='https://www.kaggle.com/harlfoxem/housesalesprediction/discussion'>https://www.kaggle.com/harlfoxem/housesalesprediction/discussion</a> and King county polygons are retrieved from <a href='https://gis-kingcounty.opendata.arcgis.com/datasets/zipcodes-for-king-county-and-surrounding-area-shorelines-zipcode-shore-area'>https://gis-kingcounty.opendata.arcgis.com/datasets/zipcodes-for-king-county-and-surrounding-area-shorelines-zipcode-shore-area</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>List of two Sf objects, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'>
<span class='co'>## Unlist in Global environment</span>
<span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://github.com/spatialanalysis/geodaData'>geodaData</a></span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>kchomesale</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/base/list2env.html'>list2env</a></span><span class='op'>(</span><span class='va'>kchomesale</span>, <span class='fu'><a href='https://rdrr.io/r/base/environment.html'>environment</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>
</div><div class='output co'>#&gt; &lt;environment: 0x7fb27c43a9a8&gt;</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/ls.html'>ls</a></span><span class='op'>(</span><span class='op'>)</span>
</div><div class='output co'>#&gt; [1] "kc_zones"           "kchomesales_points"</div><div class='input'>
<span class='co'>## Plot first listed dataset (kchomesales_points):</span>
<span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>kchomesale</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>kchomesale</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>[</span><span class='st'>"price"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='kchomesale-1.png' alt='' width='700' height='433' /></div><div class='input'>

</div></pre>
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<meta property="og:title" content="San Francisco Crime Incidents (2012) — SFcrime" />
<meta property="og:description" content="Incidents of robberies, drugs/narcotics possession or sale, vehicle theft, and vandalism for July 1 to December 31, 2012. Use list2env(SFcrime, .GlobalEnv) to unlist in working environment. The data consist of a list of four objects:
cartheft A spatial points object of class sf Dataframe with 3,384 observations, 13 variables, and a geometry column.
drugs A spatial points object of class sf Dataframe with 3,897 observations, 13 variables, and a geometry column.
robbery A spatial points object of class sf Dataframe with 2,761 observations, 13 variables, and a geometry column.
vandalism A spatial points object of class sf Dataframe with 3,430 observations, 13 variables, and a geometry column.

" />




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    <h1>San Francisco Crime Incidents (2012)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/SFcrime.R'><code>R/SFcrime.R</code></a></small>
    <div class="hidden name"><code>SFcrime.Rd</code></div>
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    <div class="ref-description">
    <p>Incidents of robberies, drugs/narcotics possession or sale, vehicle theft, and vandalism for July 1 to December 31, 2012. Use <code><a href='https://rdrr.io/r/base/list2env.html'>list2env(SFcrime, .GlobalEnv)</a></code> to unlist in working environment. The data consist of a list of four objects:</p><ul>
<li><p>cartheft A spatial points object of class sf Dataframe with 3,384 observations, 13 variables, and a geometry column.</p></li>
<li><p>drugs A spatial points object of class sf Dataframe with 3,897 observations, 13 variables, and a geometry column.</p></li>
<li><p>robbery A spatial points object of class sf Dataframe with 2,761 observations, 13 variables, and a geometry column.</p></li>
<li><p>vandalism A spatial points object of class sf Dataframe with 3,430 observations, 13 variables, and a geometry column.</p></li>
</ul>

    </div>

    <pre class="usage"><span class='va'>SFcrime</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>Each object contains the following variables:</p><dl>
<dt>	IncidntNum	</dt><dd><p>Unique identifier for each incident report</p></dd>
<dt>	X_pr	</dt><dd><p>Projected X-coordinates</p></dd>
<dt>	Y_pr	</dt><dd><p>Projected Y-coordinates</p></dd>
<dt>	Category	</dt><dd><p>Crime classification, e.g. vehicle theft, etc</p></dd>
<dt>	Descript	</dt><dd><p>Description of the crime</p></dd>
<dt>	DayOfWeek	</dt><dd><p>Day of the week that crime was reported</p></dd>
<dt>	Date	</dt><dd><p>Date of the report</p></dd>
<dt>	Time	</dt><dd><p>Time of report</p></dd>
<dt>	PdDistrict	</dt><dd><p>Police district</p></dd>
<dt>	Resolution	</dt><dd><p>Outcome of the report, e.g. None, Arrest, Unfounded, etc</p></dd>
<dt>	Location	</dt><dd><p>Street address where the crime occurred</p></dd>
<dt>	X	</dt><dd><p>X-Coordinates of crime location</p></dd>
<dt>	Y	</dt><dd><p>Y-Coordinates of crime location</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>2010 Census (population) and “Crime Incidents from 1 Jan 2003”, City of San Francisco Open Data Portal (crimes) (<a href='https://data.sfgov.org/Public-Safety/Map-Crime-Incidents-from-1-Jan-2003/gxxq-x39z'>https://data.sfgov.org/Public-Safety/Map-Crime-Incidents-from-1-Jan-2003/gxxq-x39z</a>).</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>List of four Sf objects, projected to EPSG 2227: NAD83 / California zone 3 (ftUS).</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>SFcrime</span><span class='op'>)</span>
  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>SFcrime</span><span class='op'>[[</span><span class='st'>"cartheft"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>[</span><span class='fl'>9</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='SFcrime-1.png' alt='' width='700' height='433' /></div></pre>
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<|start_filename|>docs/reference/phoenixACS.html<|end_filename|>
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<meta property="og:title" content="Income and Error in Phoeniox (2005-09) — phoenixACS" />
<meta property="og:description" content="2005-2009 ACS Margins of Error estimates from ACS for table B19301 (Per Capita Income) at the tract level for the Phoenix-Mesa-Glendale Metropolitan Statistical Area" />




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    <h1>Income and Error in Phoeniox (2005-09)</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/phoenixACS.R'><code>R/phoenixACS.R</code></a></small>
    <div class="hidden name"><code>phoenixACS.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>2005-2009 ACS Margins of Error estimates from ACS for table B19301 (Per Capita Income) at the tract level for the Phoenix-Mesa-Glendale Metropolitan Statistical Area</p>
    </div>

    <pre class="usage"><span class='va'>phoenixACS</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 985 rows, 17 variables, and a geometry column:</p><dl>
<dt>	aland10	</dt><dd><p>Tract area of land</p></dd>
<dt>	awater10	</dt><dd><p>Tract area of water</p></dd>
<dt>	geoid10	</dt><dd><p>Census Bureau’s Tract id</p></dd>
<dt>	namelsad10	</dt><dd><p>Census Bureau’s Tract name</p></dd>
<dt>	pop_dens	</dt><dd><p>Population density (including only land area)</p></dd>
<dt>	inc	</dt><dd><p>ACS estimate of per capita income</p></dd>
<dt>	inc_error	</dt><dd><p>ACS estimate of the measurement error of per capita income</p></dd>
<dt>	pct_error	</dt><dd><p>Percentage that the error represents as a share of the total p/c income estimate</p></dd>
<dt>	renter_rt	</dt><dd><p>Rate of renters as a share of the total population (in percentage)</p></dd>
<dt>	pop	</dt><dd><p>Population count</p></dd>
<dt>	white_rt	</dt><dd><p>Rate of white population (as a percentage of the total population)</p></dd>
<dt>	black_rt	</dt><dd><p>Rate of black population (as a percentage of the total population)</p></dd>
<dt>	hisp_rt	</dt><dd><p>Rate of hispanic population (as a percentage of the total population)</p></dd>
<dt>	fem_nh_rt	</dt><dd><p>Rate of females head of the household, no husband (as a percentage of the total population)</p></dd>
<dt>	vac_hsu_rt	</dt><dd><p>Rate of vacant housing units (as a percentage of total units)</p></dd>
<dt>	hsu	</dt><dd><p>Housing units count</p></dd>
<dt>	l_pct_error	</dt><dd><p>Logarithm of percentage that the error represents as a share of the total p/c income estimate</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>2005-2009 American Community Survey Summary File. U.S. Census Bureau and 2010 Population Census, Summary File 1. U.S. Census Bureau</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, EPSG:4269: NAD83.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>phoenixACS</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>phoenixACS</span><span class='op'>[</span><span class='st'>"inc"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='phoenixACS-1.png' alt='' width='700' height='433' /></div></pre>
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<meta property="og:description" content="Crime and socio-demographic data for the Clifton, Walnut Hills, Evanston, and Avondale neighborhoods in Cincinnati, OH for the last 6 months of 2008." />




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    <h1>2008 Cincinnati Crime and Socio-Demographics</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/cincinnati.R'><code>R/cincinnati.R</code></a></small>
    <div class="hidden name"><code>cincinnati.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Crime and socio-demographic data for the Clifton, Walnut Hills, Evanston, and Avondale neighborhoods in Cincinnati, OH for the last 6 months of 2008.</p>
    </div>

    <pre class="usage"><span class='va'>cincinnati</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 457 rows, 72 variables, and a geometry column:</p><dl>
 <dt>	ID	</dt><dd><p>Polygon ID</p></dd>
 <dt>	AREA	</dt><dd><p>Polygon area</p></dd>
<dt>	BLOCK	</dt><dd><p>Census block ID</p></dd>
<dt>	BG	</dt><dd><p>Census block group ID</p></dd>
<dt>	TRACT	</dt><dd><p>Census tract ID</p></dd>
<dt>	COUNTY	</dt><dd><p>5 digit code for the county (Hamilton County, Ohio). Not to be confused with zip code</p></dd>
<dt>	MSA	</dt><dd><p>Metropolitan statistical area ID</p></dd>
<dt>	POPULATION	</dt><dd><p>Population on the block</p></dd>
<dt>	MALE, FEMALE	</dt><dd><p>Population on the block broken down by gender</p></dd>
<dt>	AGE_X_Y	</dt><dd><p>Population on the block broken down by age (From A to B)</p></dd>
<dt>	MEDIAN_AGE	</dt><dd><p>Median age on the block</p></dd>
<dt>	AGE_X	</dt><dd><p>Population aged X and over</p></dd>
<dt>	MALE_X</dt><dd><p>Male population aged X and over</p></dd>
<dt>	FEMALE_X</dt><dd><p>Female population aged X and over</p></dd>
<dt>	F1_RACE	</dt><dd><p>Number of people who marked a race on the survey</p></dd>
<dt>	WHITE	</dt><dd><p>White population</p></dd>
<dt>	BLACK </dt><dd><p>Black population</p></dd>
<dt>	AMINDIAN </dt><dd><p>American Indian population</p></dd>
<dt>	ASIAN	</dt><dd><p>Asian population</p></dd>
<dt>	HAWAIIAN	</dt><dd><p>Hawaiian population</p></dd>
<dt>	OTHER_RACE	</dt><dd><p>Population self-identified as other race (non white, black, asian or hawaiian)</p></dd>
<dt>	F2_RACES	</dt><dd><p>Number of people who did not mark any race on the survey</p></dd>
<dt>	AP_WHITE	</dt><dd><p>Adjusted white population</p></dd>
<dt>	AP_BLACK	</dt><dd><p>Adjusted black population</p></dd>
<dt>	AP_AMINDIA	</dt><dd><p>Adjusted American Indian population</p></dd>
<dt>	AP_ASIAN </dt><dd><p>Adjusted Asian population</p></dd>
<dt>	AP_HAWAIIA	</dt><dd><p>Adjusted Hawaiian population</p></dd>
<dt>	AP_OTHER	</dt><dd><p>Adjusted other-race population</p></dd>
<dt>	AP_HISPANI, NOT_HISPAN	</dt><dd><p>Population breakdown by Hispanic and Non-Hispanic</p></dd>
<dt>	NH_WHITE	</dt><dd><p>Non-Hispanic white population</p></dd>
<dt>	IN_HOUSEHO	</dt><dd><p>Population living in households</p></dd>
<dt>	GROUP_QUAR	</dt><dd><p>Population living in group quarters instead of traditional households</p></dd>
<dt>	GQ_INSTU, GQ_NONINST	</dt><dd><p>Institutional population</p></dd>
<dt>	GQ_INSTU, GQ_NONINST	</dt><dd><p>Non-institutional population</p></dd>
<dt>	HOUSEHOLDS	</dt><dd><p>Number of households</p></dd>
<dt>	HH_FAMILY	</dt><dd><p>Number of family households</p></dd>
<dt>	HH_NONFAMI	</dt><dd><p>Number of non family households</p></dd>
<dt>	AVG_HHSIZE	</dt><dd><p>Average household size</p></dd>
<dt>	AVG_FAMSIZ	</dt><dd><p>Average family size</p></dd>
<dt>	HSNG_UNITS	</dt><dd><p>Number of housing units</p></dd>
<dt>	HU_OCCUPIE  </dt><dd><p>Number of housing units occupied</p></dd>
<dt>	HU_VACANT	</dt><dd><p>Number of housing units vacant</p></dd>
<dt>	OCCHU_OWNE	</dt><dd><p>Occupied housing units (owner occupied)</p></dd>
<dt>	OCCHU_RENT	</dt><dd><p>Occupied housing units (renter occupied)</p></dd>
<dt>	OWNER_SIZE	</dt><dd><p>Average household size of owner occupied housing units</p></dd>
<dt>	RENTER_SIZ	</dt><dd><p>Average household size of renter occupied housing units</p></dd>
<dt>	DENSITY	</dt><dd><p>Population density</p></dd>
<dt>	BURGLARY	</dt><dd><p>Counts of burglary</p></dd>
<dt>	ASSAULT	</dt><dd><p>Counts of assault</p></dd>
<dt>	THEFT	</dt><dd><p>Counts of theft</p></dd>
<dt>	BURG_D	</dt><dd><p>Burglary dummy: ‘1’ if there is at least 1 incident, ‘0’ otherwise</p></dd>
<dt>	ASSALT_D	</dt><dd><p>Assault dummy: ‘1’ if there is at least 1 incident, ‘0’ otherwise</p></dd>
<dt>	THEFT_D	</dt><dd><p>Theft dummy: ‘1’ if there is at least 1 incident, ‘0’ otherwise</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p><NAME>, <NAME> and <NAME> (2014). Spatial clustering overview and comparison: accuracy, sensitivity and computational expense. Annals, Association of American Geographers 104, 1134-1156.</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, units in us-ft. GRS80 /Lambert Conformal Conic.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>cincinnati</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>cincinnati</span><span class='op'>[</span><span class='st'>"BURGLARY"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='cincinnati-1.png' alt='' width='700' height='433' /></div></pre>
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    <h1>US Social Determinants of Health Data 2014</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/us_sdoh.R'><code>R/us_sdoh.R</code></a></small>
    <div class="hidden name"><code>us_sdoh.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Social Determinants of Health Data 2014 from the United States at neighborhood-level</p>
    </div>

    <pre class="usage"><span class='va'>us_sdoh</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 71901 rows, 25 variables, and a geometry column:</p><dl>
<dt>	tract_fips	</dt><dd><p>10-digit FIPS code for tract</p></dd>
<dt>	county	</dt><dd><p>County name</p></dd>
<dt>	state	</dt><dd><p>State name</p></dd>
<dt>	state_fips	</dt><dd><p>State FIPS code</p></dd>
<dt>	cnty_fips	</dt><dd><p>County FIPS code</p></dd>
<dt>	ep_pov	</dt><dd><p>Percentage of persons below poverty estimate, 2010-2014 ACS</p></dd>
<dt>	ep_unem	</dt><dd><p>Percentage of civilian (age 16+) unemployed estimate, 2010-2014 ACS</p></dd>
<dt>	ep_pci	</dt><dd><p>Per capita income estimate, 2010-2014 ACS</p></dd>
<dt>	ep_nohs	</dt><dd><p>Percentage of persons with no high school diploma (age 25+) estimate, 2010-2014 ACS</p></dd>
<dt>	ep_sngp	</dt><dd><p>Percentage of single parent households with children under 18 estimate, 2010-2014 ACS</p></dd>
<dt>	ep_lime	</dt><dd><p>Percentage of persons (age 5+) who speak English "less than well" estimate, 2010-2014 ACS</p></dd>
<dt>	ep_crow	</dt><dd><p>Percentage of households with more people than rooms estimate, 2010-2014 ACS</p></dd>
<dt>	ep_nove	</dt><dd><p>Percentage of households with no vehicle available estimate, 2010-2014 ACS</p></dd>
<dt>	rent_1	</dt><dd><p>Percentage of renters estimate, 2010-2014 ACS</p></dd>
<dt>	rntov30p_1	</dt><dd><p>Percentage of renters paying over 30 pct of income towards housing estimate, 2010-2014 ACS</p></dd>
<dt>	ep_unin	</dt><dd><p>Percentage of uninsured in the total civilian noninstitutionalized population estimate, 2010- 2014 ACS</p></dd>
<dt>	ep_minrty	</dt><dd><p>Percentage minority (all persons except white, non-Hispanic) estimate, 2010-2014 ACS</p></dd>
<dt>	ep_age65	</dt><dd><p>Percentage of persons aged 65 and older estimate, 2010-2014 ACS</p></dd>
<dt>	ep_age17	</dt><dd><p>Percentage of persons aged 17 and younger estimate, 2010-2014 ACS</p></dd>
<dt>	ep_disabl	</dt><dd><p>Percentage of civilian noninstitutionalized population with a disability estimate, 2010-2014 ACS</p></dd>
<dt>	X1_SES	</dt><dd><p>Socioeconomic Advantage Index, raw (see <a href='https://sdohatlas.github.io/'>https://sdohatlas.github.io/</a>)</p></dd>
<dt>	X2_MOB	</dt><dd><p>Limited Mobility Index, raw (see <a href='https://sdohatlas.github.io/)'>https://sdohatlas.github.io/)</a></p></dd>
<dt>	X3_URB	</dt><dd><p>Urban Core Opportunity Index, raw (see <a href='https://sdohatlas.github.io/'>https://sdohatlas.github.io/</a>)</p></dd>
<dt>	X4_MICA	</dt><dd><p>MICA Index, raw (see <a href='https://sdohatlas.github.io/'>https://sdohatlas.github.io/</a>)</p></dd>
<dt>	SDOH_CL	</dt><dd><p>1:rural affordable, 2:suburban affluent, 3:suburban affordable, 4:extreme poverty, 5:multilingual working, 6:urban core opportunity, 7:sparse areas (see <a href='https://sdohatlas.github.io/'>https://sdohatlas.github.io/</a>)</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>The U.S. Social Determinants of Health Atlas. <a href='https://sdohatlas.github.io/'>https://sdohatlas.github.io/</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>us_sdoh</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>us_sdoh</span><span class='op'>[</span><span class='st'>"ep_pov"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='us_sdoh-1.png' alt='' width='700' height='433' /></div></pre>
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    <div class="page-header">
    <h1>Denver 2010 Demographics &amp; Housing</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/denver.R'><code>R/denver.R</code></a></small>
    <div class="hidden name"><code>denver.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Demographics and housing characteristics of Denver neighborhoods (2010).</p>
    </div>

    <pre class="usage"><span class='va'>denver</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 75 rows, 55 variables, and a geometry column:</p><dl>
<dt>	NBRHD_ID	</dt><dd><p>Neighborhood ID</p></dd>
<dt>	NBRHD_NAME	</dt><dd><p>Neighborhood name</p></dd>
<dt>	POPULATION	</dt><dd><p>Population in 2010</p></dd>
<dt>	HISPANIC_2010  </dt><dd><p>Hispanic population in 2010</p></dd>
<dt>  WHITE_2010  </dt><dd><p>White population in 2010</p></dd>
<dt>  BLACK_2010  </dt><dd><p>Black population in 2010</p></dd>
<dt>  NATIVEAM_2010 </dt><dd><p>Native American population in 2010</p></dd>
<dt>  ASIAN_2010  </dt><dd><p>Asian population in 2010</p></dd>
<dt>  HAWPACIS_2010 </dt><dd><p>Hawaiian Pacific population in 2010</p></dd>
<dt>  OTHER_2010 </dt><dd><p>Other race population in 2010</p></dd>
<dt>  TWO_OR_MOR	</dt><dd><p>Population two or more races in 2010</p></dd>
<dt>	PCT_HISPAN </dt><dd><p>Hispanic as percentage of total population</p></dd>
<dt>  PCT_WHITE </dt><dd><p>White as percentage of total population</p></dd>
<dt>  PCT_BLACK </dt><dd><p>Black as percentage of total population</p></dd>
<dt>  PCT_AMERIN </dt><dd><p>American Indian as percentage of total population</p></dd>
<dt>  PCT_ASIAN </dt><dd><p>Asian as percentage of total population</p></dd>
<dt>  PCT_HAW_PA </dt><dd><p>Hawaiian Pacific as percentage of total population</p></dd>
<dt>  PCT_OTHER </dt><dd><p>Other race as percentage of total population</p></dd>
<dt>  PCT_TWO_OR	</dt><dd><p>Two or more races as percentage of total population</p></dd>
<dt>	MALE</dt><dd><p>Male Population</p></dd>
<dt>  FEMALE	</dt><dd><p>Female Population</p></dd>
<dt>	AGEX_Y</dt><dd><p>Population by age group. From X to Y</p></dd>
<dt>	PCT_LESS_1 </dt><dd><p>Percentage of population under 1 year of age</p></dd>
<dt>  PCT_65_PLUS	</dt><dd><p>Percentage of population over 65 years of age</p></dd>
<dt>	NUM_HOUSEH	</dt><dd><p>Number of households</p></dd>
<dt>	ONE_PERSON </dt><dd><p>Number of households with one member</p></dd>
<dt>  TWO_PLUS	</dt><dd><p>Number of households with more than two members</p></dd>
<dt>	FAMILY_HHL	</dt><dd><p>Number of family households</p></dd>
<dt>	HUSB_WIFE </dt><dd><p>Number of households with husband/wife head type</p></dd>
<dt>  OTHER_FAMI </dt><dd><p>Number of households non-husband/wife head type</p></dd>
<dt>  MALE_SINGLE </dt><dd><p>Number of households male-single head type</p></dd>
<dt>  FEMAL_SING </dt><dd><p>Number of households female-single head type</p></dd>
<dt>  NON_FAMILY	</dt><dd><p>Family households by household head type</p></dd>
<dt>	POP_QUAR	</dt><dd><p>Population in group quarters</p></dd>
<dt>	GQ_INSTITU </dt><dd><p>Residents of group quarters: institutional (correctional, nursing, etc.)</p></dd>
<dt>  GQ_NONINST	</dt><dd><p>Residents of group quarters: and non-institutional (military, college, etc.)</p></dd>
<dt>	HOUSING_UN	</dt><dd><p>Number of housing units</p></dd>
<dt>	OCCUPIED_H	</dt><dd><p>Number of occupied housing units</p></dd>
<dt>	VACANT_HU	</dt><dd><p>Number of vacant housing units</p></dd>
<dt>	HU_OWNED	</dt><dd><p>Number of owner-occupied housing units</p></dd>
<dt>	HU_RENTED	</dt><dd><p>Number of renter-occupied housing units</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p><a href='https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-census-neighborhood-demographics-2010'>https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-census-neighborhood-demographics-2010</a></p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>denver</span><span class='op'>)</span>

  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>denver</span><span class='op'>[</span><span class='st'>"HOUSING_UN"</span><span class='op'>]</span><span class='op'>)</span>
 <span class='op'>}</span>
</div><div class='img'><img src='denver-1.png' alt='' width='700' height='433' /></div></pre>
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<meta property="og:description" content="Business data for the Sacramento-Arden-Arcade-Roseville Metropolitan Statistical Area, CA (1998 and 2001)." />




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    <h1>Sacramento Industry Mix</h1>
    <small class="dont-index">Source: <a href='https://github.com/spatialanalysis/geodaData/blob/master/R/sacramento2.R'><code>R/sacramento2.R</code></a></small>
    <div class="hidden name"><code>sacramento2.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Business data for the Sacramento-Arden-Arcade-Roseville Metropolitan Statistical Area, CA (1998 and 2001).</p>
    </div>

    <pre class="usage"><span class='va'>sacramento2</span></pre>


    <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>

    <p>An sf data frame with 125 rows, 53 variables, and a geometry column:</p><dl>
<dt>	ZIP	</dt><dd><p>ZIP code</p></dd>
<dt>	PO_NAME	</dt><dd><p>Name of ZIP code area</p></dd>
<dt>	STATE	</dt><dd><p>State abbreviation</p></dd>
<dt>  POP2001 </dt><dd><p>Population 2001</p></dd>
<dt>	MSA	</dt><dd><p>Name of Metropolitan Statistical Area (MSA)</p></dd>
<dt>  EST98 </dt><dd><p>Total establishments in ZIP code, 1998</p></dd>
 <dt>  EMP98 </dt><dd><p>Total mid-march employees, 1998</p></dd>
 <dt>  PAY98 </dt><dd><p>Total annual payroll ($1,000), 1998</p></dd>
 <dt>  EST01 </dt><dd><p>Total establishments in ZIP code, 2001</p></dd>
 <dt>  EMP01 </dt><dd><p>Total mid-march employees, 2001</p></dd>
 <dt>  PAY01 </dt><dd><p>Total annual payroll ($1,000), 2001</p></dd>
<dt>	MAN98	</dt><dd><p>1998 total manufacturing establishments (MSA)</p></dd>
<dt>	MAN98_12	</dt><dd><p>1998 total manufacturing establishments, 1-9 employees (MSA)</p></dd>
<dt>	MAN98_39	</dt><dd><p>1998 total manufacturing establishments 10+ employees (MSA)</p></dd>
<dt>	MAN01	</dt><dd><p>2001 total manufacturing establishments (MSA)</p></dd>
<dt>	MAN01_12	</dt><dd><p>2001 total manufacturing establishments, 1-9 employees (MSA)</p></dd>
<dt>	MAN01_39	</dt><dd><p>2001 total manufacturing establishments, 10+ employees (MSA)</p></dd>
<dt>	MAN98US	</dt><dd><p>1998 total manufacturing establishments (US)</p></dd>
<dt>	MAN98US12	</dt><dd><p>1998 total manufacturing establishments, 1-9 employees (US)</p></dd>
<dt>	MAN98US39	</dt><dd><p>1998 total manufacturing establishments 10+ employees (US)</p></dd>
<dt>	MAN01US	</dt><dd><p>2001 total manufacturing establishments (US)</p></dd>
<dt>	MANUS01_12	</dt><dd><p>2001 total manufacturing establishments, 1-9 employees (US)</p></dd>
<dt>	MANUS01_39	</dt><dd><p>2001 total manufacturing establishments, 10+ employees (US)</p></dd>
<dt>	OFF98	</dt><dd><p>1998 total office establishments (MSA)</p></dd>
<dt>	OFF98_12	</dt><dd><p>1998 total office establishments, 1-9 employees (MSA)</p></dd>
<dt>	OFF98_39	</dt><dd><p>1998 total office establishments, 10+ employees (MSA)</p></dd>
<dt>	OFF01	</dt><dd><p>2001 total office establishments (MSA)</p></dd>
<dt>	OFF01_12	</dt><dd><p>2001 total office establishments, 1-9 employees (MSA)</p></dd>
<dt>	OFF01_39	</dt><dd><p>2001 total office establishments, 10+ employees (MSA)</p></dd>
<dt>	OFF98US	</dt><dd><p>1998 total office establishments (US)</p></dd>
<dt>	OFF98US12	</dt><dd><p>1998 total office establishments, 1-9 employees (US)</p></dd>
<dt>	OFF98US39	</dt><dd><p>1998 total office establishments, 10+ employees (US)</p></dd>
<dt>	OFF01US	</dt><dd><p>2001 total office establishments (US)</p></dd>
<dt>	OFFUS01_12	</dt><dd><p>2001 total office establishments, 1-9 employees (US)</p></dd>
<dt>	OFFUS01_39	</dt><dd><p>2001 total office establishments, 10+ employees (US)</p></dd>
<dt>	INFO98	</dt><dd><p>1998 total information establishments (MSA)</p></dd>
<dt>	INFO98_12	</dt><dd><p>1998 total information establishments, 1-9 employees (MSA)</p></dd>
<dt>	INFO98_39	</dt><dd><p>1998 total information establishments, 10+ employees (MSA)</p></dd>
<dt>	INFO01	</dt><dd><p>2001 total information establishments (MSA)</p></dd>
<dt>	INFO01_12	</dt><dd><p>2001 total information establishments, 1-9 employees (MSA)</p></dd>
<dt>	INFO01_39	</dt><dd><p>2001 total information establishments, 10+ employees (MSA)</p></dd>
<dt>	INFO98US	</dt><dd><p>1998 total information establishments (US)</p></dd>
<dt>	INFO98US12	</dt><dd><p>1998 total information establishments, 1-9 employees (US)</p></dd>
<dt>	INFO98US39	</dt><dd><p>1998 total information establishments, 10+ employees (US)</p></dd>
<dt>	INFO01US	</dt><dd><p>2001 total information establishments (US)</p></dd>
<dt>	INFOUS01_1	</dt><dd><p>2001 total information establishments, 1-9 employees (US)</p></dd>
<dt>	INDEX	</dt><dd><p>Unique index</p></dd>
<dt>	NUMSEC	</dt><dd><p>Number of sectors represented in ZIP code</p></dd>
<dt>	PCTNGE	</dt><dd><p>National growth effect, percent (N)</p></dd>
<dt>	PCTIME	</dt><dd><p>Industry mix effect, percent (M)</p></dd>
<dt>	PCTCSE	</dt><dd><p>Competitive shift effect, percent (S)</p></dd>
<dt>	PCTGRO	</dt><dd><p>Percent growth establishments, 1998-2001 (R)</p></dd>
<dt>	ID	</dt><dd><p>Unique ZIP code ID for variables in weights matrix creation window</p></dd>

</dl>

    <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>

    <p>1998 and 2001 Zip Code Business Patterns, US Census Bureau</p>
    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Sf object, unprojected. EPSG 4326: WGS84.</p>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/ns-load.html'>requireNamespace</a></span><span class='op'>(</span><span class='st'>"sf"</span>, quietly <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://r-spatial.github.io/sf/'>sf</a></span><span class='op'>)</span>
  <span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>sacramento2</span><span class='op'>)</span>
  <span class='fu'><a href='https://r-spatial.github.io/sf/reference/plot.html'>plot</a></span><span class='op'>(</span><span class='va'>sacramento2</span><span class='op'>[</span><span class='st'>"EST01"</span><span class='op'>]</span><span class='op'>)</span>
<span class='op'>}</span>
</div><div class='img'><img src='sacramento2-1.png' alt='' width='700' height='433' /></div></pre>
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