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poetry in schools
poetryzest
Happy National Poetry Day everyone! Not that everyday isn't a poetry day... I've share this children's poem this morning on twitter and Instagram - it's a joyous poem for nanas, so full of muscle and power - and love! It's called 'When Granny', and it's the brilliant Valerie Bloom
Children's Books North interview in their Autumn Tour
Belonging Street Launch
Guest Blog for the Youth Libraries Group
I was very honoured to be invited to co-curate the brand new Manchester Poetry Library's Children's collection. It was such an inspiring task that sent me on a journey to discover new books, new poets
poetryzest@gmail.com
If you have any questions please don't hesitate to get in touch! You can reach me using this contact form.
authors visit to schools
workshops/projects


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Q: problem with array reshaping for SVM kernel visualization I am trying to make a visualization of different kernels effect of a support vector machine over a data-set but I have a problem reshaping an array size:
The dataset I am using is the following:
https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
df_train_X = pd.read_csv("X_train.txt", header=None, delim_whitespace=True)
df_train_y = pd.read_csv("y_train.txt", header=None, delim_whitespace=True)

X_train = df_train_X.values
y_train = df_train_y[0]

X = X_train
y = y_train

X = X[y != 0, :2]
y = y[y != 0]

n_sample = len(X)

np.random.seed(0)
order = np.random.permutation(n_sample)
X = X[order]
y = y[order].astype(np.float)

X_train = X[:int(.9 * n_sample)]
y_train = y[:int(.9 * n_sample)]
X_test = X[int(.9 * n_sample):]
y_test = y[int(.9 * n_sample):]

# fit the model
for kernel in ('linear', 'rbf', 'poly'):
    clf = SVC(kernel=kernel, gamma=10)
    clf.fit(X_train, y_train)

    plt.figure()
    plt.clf()
    plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired,
                edgecolor='k', s=20)

    # Circle out the test data
    plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none',
                zorder=10, edgecolor='k')

    plt.axis('tight')
    x_min = X[:, 0].min()
    x_max = X[:, 0].max()
    y_min = X[:, 1].min()
    y_max = X[:, 1].max()

    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(XX.shape)
    plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
    plt.contour(XX, YY, Z, colors=['k', 'k', 'k'],
                linestyles=['--', '-', '--'], levels=[-.5, 0, .5])

    plt.title(kernel)
plt.show()

The problem is related to Z = Z.reshape(XX.shape) since I get the following error that blocks the code execution: cannot reshape array of size 240000 into shape (200,200)
But I don't know how to solve it to perform the visualization.

redpajama_set_name: RedPajamaStackExchange


text:
The Basque triptych: From the latter to the former
Posted on March 13, 2020 by Jules & Verne
"There's only two types of music: the good one and the bad one. I make the former", quoted Jules. – "Armstrong, right? The trumpeter, not the astronaut", I asked tentatively. We were sitting in the sand, and this had been our first breakthrough in a somewhat pompous discussion that had started well before sunset: was photography art? Were our photographs art?
Mural "Al hilo del Tiempo", Vitoria-Gasteiz,Basque Country, Spain (PPL1-Corrected)
Jules and I grew up in very different places, but we shared the same hero with all other nerdy kids from the nineties: Richard Feynman, the Nobel-winning theoretical physicist that also happened to write cracking popular science books.
Gabriel and a cautious female mallard (Anas platyrhynchos) at Ataria, Basque Country, Spain (PPL1-Corrected)
Downpour at Ataria, Basque Country, Spain (PPL3-Altered)
One of his stories was particularly popular among kids fearful that devoting their life to science would turn them into insensitive lumps of coal. One time, Richard and a painter friend were taking a stroll through the woods and found a beautiful flower. The painter looked at it and said: "Only I as an artist can appreciate the true beauty of this flower. You scientists take it all apart and make it dull". Richard would have nothing of that: "That's crazy. I too have a sense of aesthetics, one that allows me to not only appreciate the beauty of these shapes and colours, but also understand the complexity of its inner workings and the way it has evolved. Knowledge only adds to that sense of awe, it never subtracts from it".
Vizcaya Bridge montage, Portugalete, Basque Country, Spain (PPL3-Altered)
So, ushered forward by Louis Armstrong and Richard Feynman's words of encouragement, we proudly self-proclaimed ourselves artists. But… were we any good at it?
Gabriel and Verne at Gorliz Beach, Basque Country, Spain (PPL3-Altered)
Jules and Gabriel at Gorliz Beach, Basque Country, Spain (PPL1-Corrected)
The next clue was delivered by Gabriel, who tripped over the tripod and sent our camera hurtling to the sand, lens first. After informing him that the repair would come out of his piggy bank, I turned to Jules. – "I'm sure we'll leave this beach with some good-looking pictures, but what did we do besides pressing the shutter? How do you go from good-looking to good? How do you create something unique?". Searching for uniqueness on a discussion based on quotes from others was an odd proposition, but we wouldn't let logic get in the way of things.
Sunset at Gorliz Beach, Basque Country, Spain (PPL3-Altered)
After a long silence, occasionally interrupted by Gabriel's impersonation of an excavator, Jules finally spoke. – "I think a unique photograph leaves you with a question, rather than providing you with an answer". She picked up the camera and scrolled through its pictures. – "This one, for instance. It's a pretty sunset, but it doesn't add anything to the scene. It's merely a faithful reproduction".
Vizcaya Bridge, Portugalete, Basque Country, Spain (PPL3-Altered)
Vizcaya Bridge, Portugalete, Basque Country, Spain (PPL2-Enhanced)
We browsed through our favourite photographs and agreed that most of them spoke more than they listened. They were tidy classifieds, diligently listing all the characteristics of a second-hand car.
Gabriel at Gorliz Beach, Basque Country, Spain (PPL3-Altered)
Some were different though. A sequence of images of Gabriel potty training, briefly looking directly into the camera before closing the bathroom door. A dew-covered little leaf, reflecting a glimpse of its surroundings. A foggy landscape, all white except for a few indistinct shapes in the background. These very few images left us wondering about what hadn't been captured, rather than about what had.
Jules and Gabriel at Ataria, Basque Country, Spain (PPL1-Corrected)
Portugalete harbour, Basque Country, Spain (PPL1-Corrected)
"There's only two types of photographs: the good ones and the bad ones. We make the latter", I concluded. It wasn't a sad or frustrated statement, but a rather enthusiastic one. We now had a plan that could maybe one day take us from the latter to the former. There was hope for a couple of lumps of coal.
Sunset montage at Gorliz Beach, Basque Country, Spain (PPL3-Altered)
ArtBasque CountryEuropeExplorationPhoto EssayPhotographySpainStorytellingTravel20 comments
The Basque triptych: Bilbao anomaly
The old, the young and the gull – a tale of the Ciés Islands
20 thoughts on "The Basque triptych: From the latter to the former"
Wonderful pictures ☺
Thank you Flavia 🙂
– Verne
You're very welcome Verne ☺
I love the Basque country and these are stunning photos
Thank you Sheree!
I like all your photos, whether they count as art to you or not. Yours are touchingly beautiful. Mine are utilitarian, used to illustrate a story.
Thank you Anne. Perhaps the true challenge is to make beautiful utilitarian photos, or photos that are beautiful despite showing mundane things. If we can't get there, perhaps Gabriel can: a few months back we gave him an old point-and-shoot camera, and he now goes around photographing everything. The images of his favourite Legos are particularly striking 🙂
That's marvelous that you've given Gabriel a camera.
awtytravels says:
Your photos ARE art and too are your maps!
And I'd say that Feynman's painter friend is 110% wrong. Science can be art! It can be music!
Thank you Fabrizio, you're too kind! Now we'll have to include a map on our next post, no question about it…
The painter was at least 112.3% wrong 🙂
Bama says:
"Knowledge only adds to that sense of awe, it never subtracts from it." I can relate to this. Learning deeper about how ancient temples were made and about the societies that built them, among other things, really makes traveling, photography and writing even more interesting. Although sometimes this can make me feel lonely since many people seem to not bother to care. Speaking of art, I agree with Fabrizio. To me, your photos and maps are art.
Thank you Bama!
Good point on the link between history and travelling. I particularly enjoy our discussions on 16th century events and the intersections between Indonesian and Portuguese history!
Dave Ply says:
I've seen "art" that's managed to land in art museums or galleries that I would question if it qualifies as art. Some of it, especially modern art, looks like someone randomly knocked over their trash bin and called the results artistic.
If they can claim that's art, even ho-hum photos should qualify too. (Although I'd argue any true piece of art should evoke emotion, albeit even a little.)
And I agree with Richard Feynman. Maybe that's why I like macro as well as the big landscapes.
There's two things that remain complete mysteries to me: how to blow bubbles with gum and, indeed, how to distinguish "art" from art.
Math is so much easier than either. A mathematical theorem is either right or wrong, there isn't anything in the middle. The former is inherently beautiful, the latter is just an ink blob on a piece of paper.
Joaquim Gaspar says:
The Portuguese poet Fernando Pessoa wrote that Newton's binomial theorem was as beautiful as the Venus de Milo. And most mathematicians would agree that beauty is a valid criterion (if not the best) for mathematical correctness. I also fully agree with Feynman reply although I feel a bit envious for not having the capacity for that kind of quick and ruthless reaction… By the way, I'm also a macro photographer.
And yet, Fernando Pessoa stated that "Newton's binomial theorem was as beautiful as the Venus de Milo". Also, many mathematicians would agree on that beauty is a valid criterion (if not the best) of mathematical truth. Or rightness, if you prefer.
Also the other way around i.e. that mathematical truth is the valid criterion for beauty.
Manja Mexi Mexcessive says:
Fascinating train of thought, with a rather bleak upside down finish. Whereas I don't think that turning things upside down necessarily helps, I can say that the first two photos, especially the second one, are really good. I also love Gabriel with the duck and Gabriel with Jules because they are love. ❤ But who am I to say – I don't even know who Feynman was (is?). As for art or not art – I have given up on that, those unconvincing white squares on white walls always leave me wondering who is crazy here. I would say – keep doing it with love, it comes across.
Great point on love! So good that I'll forgive you for not knowing who Feynman was 🙂
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redpajama_set_name: RedPajamaCommonCrawl


text:
Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity
Loring J. Thomas, View ORCID ProfilePeng Huang, View ORCID ProfileFan Yin, View ORCID ProfileXiaoshuang Iris Luo, View ORCID ProfileZack W. Almquist, View ORCID ProfileJohn R. Hipp, and View ORCID ProfileCarter T. Butts
PNAS September 29, 2020 117 (39) 24180-24187; first published September 10, 2020; https://doi.org/10.1073/pnas.2011656117
Loring J. Thomas
aDepartment of Sociology, University of California, Irvine, CA, 92697;
Peng Huang
ORCID record for Peng Huang
Fan Yin
bDepartment of Statistics, University of California, Irvine, CA, 92697;
ORCID record for Fan Yin
Xiaoshuang Iris Luo
cDepartment of Criminology, Law, and Society, University of California, Irvine, CA, 92697;
ORCID record for Xiaoshuang Iris Luo
Zack W. Almquist
dDepartment of Sociology, Center for Studies in Demography and Ecology, Center for Statistics and Social Sciences, eScience, University of Washington, Seattle, WA, 98195;
ORCID record for Zack W. Almquist
John R. Hipp
ORCID record for John R. Hipp
Carter T. Butts
eDepartment of Computer Science, University of California, Irvine, CA, 92697;
fDepartment of Electrical Engineering and Computer Science, University of California, Irvine, CA, 92697
ORCID record for Carter T. Butts
For correspondence: buttsc@uci.edu
Edited by Douglas S. Massey, Princeton University, Princeton, NJ, and approved August 18, 2020 (received for review June 6, 2020)
We examine the effects of an uneven population distribution on the spread of the COVID-19 disease spread, using a diffusion model based on interpersonal contact networks. Taking into account spatial heterogeneity, the spread of COVID-19 is much "burstier" than in standard epidemiological models, with substantial local disparities in timing and severity and long lags between local outbreaks. We show that spatial heterogeneity may produce dramatic differences in social exposures to those with the illness, and may stress health care delivery systems in ways that are not well captured by standard SIR-like models.
Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible–infectious–recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.
Since its emergence at the end of 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly to all portions of the globe, infecting over 20 million people as of mid-August 2020 (1). The disease caused by this virus, denoted COVID-19, generally manifests as a respiratory illness that is spread primarily via airborne droplets. While most cases of COVID-19 are nonfatal, a significant fraction of those infected require extensive supportive care, and the mortality rate is substantially higher than more common infectious diseases such as seasonal influenza (2). Even for survivors, infection can lead to long-term damage to the lungs and other organs, leading to long convalescence times and enhanced risk of secondary complications (3, 4). By early March of 2020, COVID-19 outbreaks had appeared on almost every continent, including significant clusters within many cities (5). In the absence of an effective vaccine, public health measures to counteract the pandemic in developed nations have focused on social distancing measures that seek to slow diffusion sufficiently to avoid catastrophic failure of the health care delivery system. Both the planning and public acceptance of such measures have been highly dependent upon the use of epidemiological models to probe the potential impact of distancing interventions, and to anticipate when such measures may be loosened with an acceptable level of public risk. As such, the assumptions and behavior of COVID-19 diffusion models are of significant concern.
Currently, dominant approaches to COVID-19 modeling (6⇓–8) are based on compartment models (often called SIR models, after the conventional division of the population into susceptible, infected, and recovered groups in the most basic implementations) that implicitly treat individuals within a population as geographically well mixed. While some such models include differential contact by demographic groups (e.g., age), and may treat states, counties, or, occasionally, cities as distinct units (e.g., work by ref. 9), those models presently in wide use do not incorporate spatial heterogeneity at local scales (e.g., within cities). Past work, however, has shown evidence of substantial heterogeneity in social relationships at regional, urban, and suburban scales (10⇓–12), with these variations in social network structure impacting outcomes as diverse as regional identification (13), disease spread (14), crime rates (15), neighborhood identification, and development (12, 16). If individuals are not socially "well-mixed" at local scales, then it is plausible that diffusion of SARS-CoV-2 via interpersonal contacts will likewise depart from the uniform mixing characteristic of the SIR models. Indeed, at least one computational study (17) using a fairly "generic" (non-COVID) diffusion process on realistic urban networks has shown considerable nonuniformity in diffusion times, suggesting that such effects could hypothetically be present. Variations across local regions on the pandemic timing, severity, and the hospital load could have huge impacts on the social outcomes of different population groups (e.g., racial/ethnic groups) in the pandemic, given the heterogeneity of their spatial distribution in urban and suburban areas (18, 19). However, it could also be hypothesized that such effects would be small perturbations to the broader infection curve captured by conventional compartment models, with little practical importance. The question of whether these effects are likely to be present for COVID-19, and, if so, their strength and size, has, to date, remained open.
In this paper, we examine the potential impact of local spatial heterogeneity on COVID-19, modeling the diffusion of SARS-CoV-2 in populations whose contacts are based on spatially plausible network structures. We focus here on the urban context, examining 19 different cities in the United States. We simulate the population of each city in detail (i.e., at the individual level), simulating hypothetical outbreaks on the contact network in each city in the absence of measures such as social distancing. Despite allowing the population to be well mixed in all other respects (i.e., not imposing mixing constraints based on demographic or other characteristics), we find that spatial heterogeneity alone is sufficient to induce substantial departures from spatially homogeneous SIR behavior. Among the phenomena observed are "long lag" outbreaks that appear in previously unharmed communities after the aggregate infection wave has largely subsided, frequently low correlations between infection timing in spatially adjacent communities, and distinct subpatterns of outbreaks found in some urban areas that are uncorrelated with the broader infection pattern. Gaps between infection peaks at the intraurban level can be large, for example, on the order of weeks or months in extreme cases, even for communities that are within kilometers of each other. Such heterogeneity is potentially consequential for the management of health care delivery services: As we show, using a simple "catchment" model of hospital demand, local variations in infection timing can easily overload hospitals in some areas, generating "hospital deserts" (20), while leaving others relatively empty (absent active reallocation of patients). Likewise, we show that individuals' social exposures to others who are morbid or deceased vary greatly over the course of the pandemic, potentially leading to differences in risk assessment and bereavement burden for persons residing in different locations. Differences in outbreak timing and severity may exacerbate health disparities (since, e.g., surge capacity varies by community) and may even affect perception of and support for prophylactic behaviors among the population at large, with those in so-far untouched communities falsely assuming that the pandemic threat is either past or was exaggerated to begin with, or attributing natural variation in disease timing to the impact of health interventions.
We note at the outset that the models used here are intended to probe the hypothetical impact of spatial heterogeneity on COVID-19 diffusion within particular scenarios, rather than to produce high-accuracy predictions or forecasts. For the latter applications, it is desirable to incorporate many additional features that are here simplified to facilitate insight into the phenomenon of central interest. In particular, we do not incorporate either demographic effects or social distancing (21, 22), allowing us to consider a setting that is as well mixed as possible (and hence as close as possible to an idealized SIR model), with the exception of spatial heterogeneity. As we show, even this basic scenario is sufficient to produce large deviations from the SIR model. Despite the simplicity of our models, we do note that the approach employed here could be integrated with other factors and calibrated to produce models intended for forecasting or similar applications.
Spatial Network Data.
Networks are generated using population distributions from the most recent US Census in 2010. Network construction followed the same methodology as Butts et al. (23). Hospital information was obtained from the Homeland Infrastructure Foundation-Level Data (HIFLD) database (24). HIFLD is an initiative that collects geospatial information on critical infrastructure across multiple levels of government. We employ the national-level hospital facility database, which contains locations of hospitals for the 50 US states; Washington, DC; and US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands; underlying data are collated from various state departments or federal sources (e.g., Oak Ridge National Laboratory). We employ all hospitals within our 19 target cities, excluding facilities closed since 2019. Latitude/longitude coordinates and capacity information were employed to create a spatial database that includes information on the number of beds in each hospital. The capacity information includes the number of beds that each hospital has available, and can be used to assess strain that a surge in hospitalizations could create.
The dates of the first confirmed case and all of the death cases for King County, where Seattle is located, were obtained from The New York Times, based on reports from state and local health agencies (25). The death rate was calculated based on population size of each county from the 2018 American Community Survey, and employed to calibrate the infection rate (the only free parameter in the models used here); details are provided in SI Appendix.
We ran 10 replicates of the COVID-19 diffusion process in each of our 19 cities, seeding with 25 randomly selected infections in each replicate and following the course of the diffusion until no infectious individuals remained. Simulations were performed using a combination of custom scripts for the R statistical computing system (26) and the statnet library (27⇓–29). Analyses were performed using R.
COVID-19 is typically transmitted via direct contact with infected individuals, with the greatest risk occurring when an uninfected person is within approximately six feet of an infected person for an extended period. Such interactions can be modeled as events within a social network, where individuals are tied to those with whom they have a high hazard of intensive interaction. In prior work, this approach has been successfully employed for modeling infectious diseases ranging from HIV (30) and influenza (31) to Zika (32). To model networks of potential contacts at scale, we employ spatial network models (33), which are both computationally tractable and able to capture the effects of geography and population heterogeneity on network structure (23). Such models have been successfully used to capture social phenomena ranging from neighborhood-level variation in crime rates (15) and regional identification (13) to the flow of information among homeless persons (34).
The spatial network models used here allow for complex social dependence through a kernel function, referred to as the social interaction function (SIF). The SIF formally defines the relationship between two individuals based on spatial proximity. For example it has been shown that many social interaction patterns obey the Zipf law (35), where individuals are more likely to interact with others close by rather than far away [a pattern that holds even for online interactions (10)]. Here, we use this approach to model a network that represents the combination of frequent interactions due to ongoing social ties and contacts resulting from frequent incidental encounters (e.g., interactions with neighbors and community members).
We follow the protocol of refs. 15 and 23 to simulate social network data that combine the actual distribution of residents in a city with a prespecified SIF. We employ the model of ref. 15 with decennial Census data to produce large-scale social networks for 19 cities and counties in the United States—providing a representation of major urban areas in the United States (SI Appendix). Given these simulated networks, we then implement an individual-level SIR-like framework to examine COVID-19 diffusion. At each moment in time, each individual can be in a susceptible, infected but not infectious, infectious, deceased, or recovered state. The disease diffuses through the contact network, with currently infectious individuals infecting susceptible neighbors as a continuous time Poisson process with a rate estimated from mortality data (SI Appendix); recovered or deceased individuals are not considered infectious for modeling purposes. Upon infection, an individual's transitions between subsequent states (and into mortality or recovery) are governed by waiting time distributions based on epidemiological data as described in SI Appendix. To begin each simulated trajectory, we randomly infect 25 individuals, with all others being considered susceptible. Simulation proceeds until no infectious individuals remain.
From the simulated trajectory data, we produce several metrics to assess spatial heterogeneity in disease outcomes. First, we present infection curves for illustrative cities, showing the detailed progress of the infection and its difference from what an SIR model would posit. We also present choropleth maps showing spatial variation in peak infection times, as well as the correlations between the infection trajectory within local areal units and the aggregate infection trajectory for the city as a whole. While an SIR model would predict an absence of systematic variation in the infection curves or the peak infection day for different areal units in the same city, geographically realistic models show considerable disparities in infection progress from one neighborhood to another. To quantify the degree of heterogeneity more broadly, we examine spatial variation in outcomes for each of our city networks. We show that large variations in peak infection days across tracts are typical (often spanning weeks or even months), and that overall correlations of within-tract infection trajectories with the aggregate urban trajectory are generally modest (a substantial departure from what would be expected from an SIR model).
In addition to these relatively abstract metrics, we also examine a simple measure of the potential load on the health care system in each city. Given the locations of each hospital in each city, we attribute infections to each hospital using a Voronoi tessellation (i.e., under the simple model that individuals are most likely to be taken to the nearest hospital if they become seriously ill). Examination of the potential hospital demand over time shows substantial differences in load, with some hospitals severely impacted while others have few cases. Finally, we consider the social exposure of individuals to COVID-19, by computing the fraction of individuals with a personal contact who is respectively morbid or deceased. Our model shows considerable differences in these metrics over time, revealing that the pandemic can appear very different to those "on the ground"—evaluating its progress by its impact on their own personal contacts—than what would be suggested by aggregate statistics.
Smooth Aggregate Infection Trajectories Can Mask Local Outbreak Dynamics.
When taken over even moderately sized regions, aggregate infection curves can appear relatively smooth. Although this suggests homogeneous mixing (as assumed, e.g., by standard SIR models), appearances can be deceiving. Fig. 1 shows typical realizations of infection curves for two cities (Seattle, WA, and Washington, DC), showing both the aggregate trajectory (red) and trajectories within individual Census tracts (black). While the infection curves in both cases are relatively smooth, and suggestive of a fairly simple process involving a sharp early onset followed by an initially sharp but mildly slowing decline in infections, within-tract trajectories tell a different story. Instead of one common curve, we see that tracts vary wildly in onset time and curve width, with some tracts showing peaks weeks or months after the initial aggregate spike has passed.
(Top Left) Infection curves for Seattle, WA. The red line is the curve for the whole city, while the black lines are the infection curves for each tract in the city. While the red curve is relatively smooth, this smoothness hides a significant amount of heterogeneity in the timing of the infection curves for each census tract. (Top Right) Infection curves for Washington, DC. As with Seattle, the city-level curve conceals considerable spatial variability in the infection's progress. (Bottom Left) Histogram showing the mean pairwise correlation of infection curves for each tract within each city, across our entire sample. The infection curve in any given tract is likely to have a correlation of only around 0.2 with any other tract in the city. This histogram includes a single data point for each tract in the sample. (Bottom Right) Histogram of variance accounted for by the principal component of the standardized tract-level curve set. None of the principal components account for more than 60% of the variance, with most accounting for around only 35% of the total variance. The data points included here include a single amount of variance explained for each city.
The cases of Fig. 1 are emblematic of a more systematic phenomenon: The progress of the infection within any given areal unit often has relatively little relationship to its progress in the city as a whole. Fig. 1, Bottom assesses this phenomenon over our entire sample, using two different consensus metrics. First, we simply compute the correlation between the infection curves in each pair of tracts (assessed at daily resolution), taking the mean for each tract of its correlation with all other tracts within the city; if the progress of the infection were uniform across the city, the mean correlations would be large and positive. Second, we provide a more direct assessment of the extent to which the set of infection curves can be summarized by a common pattern by taking the variance on the first principal component of the correlation matrix generated from the tract-level correlations discussed immediately above. As before, where different parts of the city experience similar patterns of growth and decline in infections, we expect the dimension of greatest shared variance to account for the overwhelming majority of variation in infection rates. Contrary to these expectations, however, Fig. 1 shows that there is little coherence in tract-level infection patterns. Mean correlations of local infection curves across tracts typically range from ∼0 to 0.5, with a mean of approximately 0.2, indicating very little correspondence between infection timing in one tract and that of another. The principal component analysis tells a similar story: Overall, we see that the first component accounts for relatively little of the total variance in trajectories, with, on average, only around 35% of variation in infection curves lying on the first principal component (and no observed case of the first component accounting for more than 60% of the variance). Interestingly, this variation is not explained by time required for the diffusion process to reach each tract (SI Appendix, Fig. S4), in contrast to the hypothesized importance of similar delays in a cross-national context (9). This confirms that local infection curves are consistently distinct, with behavior that is only weakly related to infections in the city as a whole. This is a substantially different scenario than what is commonly assumed in traditional SIR models.
Peak Infection Days Can Vary Substantially, Even among Nearby Regions.
These differences in local infection curves are a consequence of the unevenness of the "social fabric" that spans the city: While the disease can spread rapidly within regions of high local connectivity, it can easily become stalled upon reaching the boundaries of these regions. Further transmission requires that a successful infection event occur via a bridging tie, an event with a potentially long waiting time. Such delays create potential opportunities for public health interventions (trace/isolate/treat strategies), but they can also create a false sense of security for those on the opposite side of the bridge (who may incorrectly assume that their area was passed over by the infection). Indeed, examining the time to peak infection across the cities of Seattle and Washington, DC (Fig. 2) shows that, while peak times are visibly autocorrelated, tracts with different peak times frequently border each other. Residents on opposite sides of the divide may be exposed to very different local infection curves, making risk assessment difficult.
(Left) Choropleth showing the peak day of infection in each tract in the city of Seattle. The map shows significant variability in peak times, with nearby regions sometimes having sharply different patterns. In outlying parts of Seattle, the infection does not peak until almost a year past the first infections, while, in the more eastern and central parts of the city, the infection peaks much earlier. (Right) Times to peak infection for Washington, DC tracts. The southern and eastern part of the city has infections that are more delayed than in the central and northern parts of the city. Both of these maps show that there is a high degree of spatial heterogeneity present in the infection curves.
The cases of Seattle and Washington, DC are not anomalous. Looking across multiple trajectories over our entire sample, Fig. 3 shows consistently high variation in per-tract peak infection times for nearly all study communities. (This variation is also seen within individual trajectories, as shown in SI Appendix, Fig. S2.) Although peak times in some cities are concentrated within an interval of several days to a week, it is more common for peak times to vary by several months or longer. Extreme delays in peak time arise from "slow burn" trajectories associated with very long infection chains, in which sequential transmission to only one or two alters at a time can sustain infection for greatly extended periods. Such gaps can arise naturally in inhomogeneous networks, but are far from what would be expected under uniform local mixing.
Marginal distributions of days to peak infection by tract, across 10 simulated trajectories. Although locales vary both in terms of overall median peak time and range of tract-level variation, large differences in peak time are nearly ubiquitous. (Trajectory specific distributions are shown in Fig. S2.)
Heterogeneous Impact Timing May Affect Hospital Load.
Variation in the timing of COVID-19 impacts across the urban landscape has potential ramifications for health care delivery, creating unequally distributed loads that overburden some providers while leaving others with excess resources. To obtain a sense of how spatial heterogeneity in the infection curve could potentially impact hospitals, we employ a simple "catchment" model in which seriously ill patients are taken to the nearest hospital, subsequently recovering and/or dying as assumed throughout our modeling framework. Based on prior estimates (36), we assume that 14% of all infections are severe enough to require hospitalization (robustness to alternative rate estimates is shown in SI Appendix). While hospitals draw from (and hence average across) areas that are larger than tracts, the heterogeneity shown in Fig. 1 suggests the potential for substantial differences in hospital load over time. Indeed, our models suggest that such differences will occur. Fig. 4 shows the number of patients arriving at each hospital in Seattle and Washington, DC during a typical simulation trajectory. While some hospitals do have demand curves that mirror the city's overall infection curve, others show very different patterns of demand. In particular, some hospitals experience relatively little demand in the early months of the pandemic, only to be hit hard when infections in the city as a whole are winding down.
(Top Left) Numbers of infections attributed to each hospital in the city of Seattle, with each curve representing a different hospital. Hospital peak demand times vary markedly, with some getting the majority of their hospitalizations before day 100, and others peaking almost a year into the pandemic. (Top Middle) Hospitalizations in Washington, DC. As in Seattle, each hospital has a unique demand trajectory, with some hospitals not getting their peak of infections until more than a year after the infection begins. (Bottom Left) Hospital strain in Seattle, WA. Values closer to zero indicate that hospitals are more strained and have fewer open beds, while lower values suggest more resources are available; color varies from blue (low average strain) to red (high average strain). Much like the number of infections, there is a high degree of heterogeneity present here, with hospitals freeing up resources at different points across the first year of the pandemic. (Bottom Middle) Hospital strain for Washington, DC. Most hospitals get overwhelmed in the first 25 days of the pandemic, but then are able to recover at different times, usually within the second hundred days of the pandemic; some, however, are hit hard by a second wave, and others remain overwhelmed for several months. (Right) Marginal distribution of number of days without available beds, for all hospitals in our sample. While most hospitals will have only brief periods of overload, some will be at or over capacity for the entire pandemic, potentially several years.
Just as hospital load varies, hospital capacities vary as well. As a simple measure of strain on hospital resources, we consider the difference between the number of COVID-19 hospitalizations and the total capacity of the hospital (in beds), truncating at zero when demand outstrips supply. (For ease of interpretation as a measure of strain, we take the difference such that higher values indicate fewer available beds.) Using data on hospital locations and capacities, we show, in Fig. 4, strain on all hospitals in Seattle and Washington, DC during a typical infection trajectory. While some hospitals are hardest hit early on (as would be expected from the aggregate infection curve), others do not peak for several months. Likewise, hospitals proximate to areas of the city with very different infection trajectories experience natural "curve flattening," with a more distributed load, while those that happen to draw from positively correlated areas experience very sharp increases and declines in demand. These conditions in some cases combine to keep hospitals well under capacity for the duration of the pandemic, while others are overloaded for long stretches of time. These marked differences in strain for hospitals within the same city highlight the potentially complex consequences of heterogeneous diffusion for health care providers.
Looking across cities, we see the same high-variability patterns as observed in Seattle and Washington. In particular, we note that local variation in disease timing leads to a heavy-tailed distribution for the duration at which hospitals will be at capacity. Fig. 4 shows the marginal distribution of hospital overload periods (defined as total number of days at capacity during the pandemic), over the entire sample. While the most common outcome is for hospitals to be stressed for a brief period (not always to the breaking point), a significant fraction of hospitals end up being overloaded for months—or even, in a small fraction of cases, nearly the whole duration of the pandemic.
It should be reiterated that the hospital load model used here is extremely simplified, and that we are employing a no-mitigation scenario. However, these results quite graphically demonstrate that the importance of curve-flattening interventions does not abate once geographical factors are taken into account. On the other hand, these results suggest that differences in hospital load may be substantially more profound than would be anticipated from uniform mixing models, creating logistical challenges and possibly exacerbating existing differences in resource levels across hospitals. At the same time, such heterogeneity implies that resource sharing and patient transfer arrangements could prove more effective as load management strategies than would be suggested by spatially homogeneous models, as hospitals are predicted to vary considerably in the timing of patient demand.
Social Exposures to Morbidity and Mortality Vary by Location.
In addition to health care strain, the subjective experience of the pandemic will potentially differ for individuals residing in different locations. In particular, social exposures to outcomes such as morbidity or mortality may shape individuals' understandings of the risks posed by COVID-19, and their willingness to undertake protective actions to combat infection. Such exposures may furthermore act as stressors, with potential implications for physical and/or mental health. As a simple measure of social exposure, we consider the question of whether a focal individual (ego) either has experienced a negative outcome themself or has at least one personal contact (alter) who has experienced the outcome in question. (Given the highly salient nature of COVID-19 morbidity and mortality, we focus on the transition to first exposure rather than, e.g., the total number of such exposures, as the first exposure is likely to have the greatest impact on ego's assessment of the potential severity of the disease.)
To examine how social exposure varies by location, we compute the fraction of individuals in each tract who are socially exposed to morbidity or mortality. Fig. 5 shows these proportions for Baltimore, MD, over the course of the pandemic. As with other outcomes examined here, we see considerable variation in timing, with many tracts seeing a rapid increase in exposure to infections, while others go for weeks or months with relatively few persons having a personal contact with the disease. Another notable axis of variation is sharpness. In many tracts, the fraction of individuals with at least one morbid contact transitions from near zero to near one within a matter of days, creating an extremely sharp social transition between the "preexposure world" (in which almost no one present knows someone with the illness) to a "postexposure world" in which almost everyone knows someone with the illness). By contrast, other tracts show a much more gradual increase (sometimes punctuated by jumps), as more and more individuals come to know someone with the disease. In a few tracts that are never hit hard by the pandemic, few people ever have an infected alter; residents of these areas obviously have a very different experience than those of high-prevalence tracts. These distinctions are even more stark for mortality, which takes longer to manifest and which does so much more unevenly. Tracts vary greatly in the fraction of individuals who ultimately lose a personal contact to the disease, and in the rapidity with which that fraction is reached. In many cases, it may take a year or more for this quantity to be realized; until that point, many residents may be skeptical to the notion that the pandemic poses a great risk to them personally.
(Left) Trajectories showing the fraction of people in each tract in Baltimore who have an infected person in their personal network across time. We see a large degree of spatial heterogeneity, as some tracts are more insulated from others in terms of social exposure. However, by the end of the pandemic, most people across all tracts have been exposed to someone who has had the disease. (Right) The fraction of persons in each tract who have an alter who died from COVID-19 in their personal network. On average, only around 20 to 30% of people in any given tract know someone who died, by the end of the pandemic, although this varies widely across tracts.
By way of assessing the milieu within each tract, it is useful to consider the "cross-over" point at which at least half of the residents who will be socially exposed in a given tract have been socially exposed to either COVID-19 morbidity or mortality. Fig. 6 maps these values for Baltimore, MD. It is immediately apparent that social exposures are more strongly spatially autocorrelated than other outcomes considered here, due to the presence of long-range ties within individuals' personal networks. Even so, however, we see strong spatial differentiation, with residents in the urban core being exposed to both morbidity and mortality much more quickly than those on the periphery. This suggests that the social experience of the pandemic will be quite different for those in city centers compared to those in more outlying areas, with the latter taking far longer to be exposed to serious consequences of COVID-19. This may manifest in differences in willingness to adopt protective actions, with those in the urban core being more highly motivated to take action (and perhaps resistant to rhetoric downplaying the severity of the disease) than those on the outskirts of the city.
(Left) Choropleth showing the time for half of those in each tract to be socially exposed to COVID-19 morbidity in Baltimore, MD. The central parts of the city are exposed far sooner than the northwestern part of the city. (Right) Choropleth showing the time for half of those in each tract to be socially exposed to COVID-19 mortality. Central Baltimore is exposed to deaths in personal networks far sooner than the more outlying areas of the city.
Our simulation results all underscore the potential effects of local spatial heterogeneity on disease spread. The spatial heterogeneity driving these results occurs on a very small scale (i.e., Census blocks), operating well below the level of the city a whole. As the infection spreads, relatively small differences in local network connectivity and the prevalence of bridging ties driven by uneven population distribution can lead to substantial differences in infection timing and severity, leading different areas in each city to have vastly different experiences of the pandemic. Resources will be utilized differently in different areas, as some areas will experience the bulk of their infections far later than others, and the subjective experience of a given individual regarding the pandemic threat may differ substantially from someone in another area. These behaviors are in striking contrast to what is assumed by models based on the assumption of spatially homogeneous mixing, which posit uniform progress of the infection within local areas.
As noted at the outset, our model is based on a no-mitigation scenario, and is not intended to capture the impact of social distancing. While distancing measures by definition limit transmission rates—and will hence slow diffusion—contacts occurring through spatially correlated networks like those modeled here are still likely to show patterns of heterogeneity like those described. One notable observation from our simulations is the long outbreak delay that some census tracts experience, even in the absence of social distancing. This would suggest that relaxation of mitigation measures leading to a resumption of "normal" diffusion may initially appear to have few negative effects, only to lead to deadly outbreaks weeks or months later. Public health messaging may need to stress that apparent lulls in disease progress are not necessarily indicators that the threat has subsided, and that areas "passed over" by past outbreaks could be impacted at any time.
Finally, we stress that conventional diffusion models using locally homogeneous mixing have been of considerable value in both pandemic planning and scenario evaluation. Our findings should not be taken as an argument against the use of such models. However, the observation that incorporating geographical heterogeneity in contact rates leads to radically different local behavior would seem to suggest that there is value in including such effects in models intended to capture outcomes at the city or county level. Since these are the scales on which decisions regarding infrastructure management, health care logistics, and other policies are often made, improved geographical realism could potentially have a substantial impact on our ability to reduce lives lost to the COVID-19 pandemic.
Data Availability.
Social networks and analysis code data have been deposited in Harvard Dataverse (https://doi.org/10.7910/DVN/B9XKSR) (37).
This material is based on research supported by NSF Awards IIS-1939237 and SES-1826589 to C.T.B., and by a University of California, Irvine Council on Research, Computing and Libraries grant.
↵1To whom correspondence may be addressed. Email: buttsc{at}uci.edu.
Author contributions: J.R.H. and C.T.B. designed research; L.J.T., P.H., F.Y., and Z.W.A. performed research; L.J.T., P.H., Z.W.A., and C.T.B. contributed new reagents/analytic tools; L.J.T., P.H., F.Y., X.I.L., and Z.W.A. analyzed data; and L.J.T., P.H., F.Y., X.I.L., Z.W.A., J.R.H., and C.T.B. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2011656117/-/DCSupplemental.
Copyright © 2020 the Author(s). Published by PNAS.
This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
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You are going to email the following Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity
Loring J. Thomas, Peng Huang, Fan Yin, Xiaoshuang Iris Luo, Zack W. Almquist, John R. Hipp, Carter T. Butts
Proceedings of the National Academy of Sciences Sep 2020, 117 (39) 24180-24187; DOI: 10.1073/pnas.2011656117


Huawei Mate 10 Pro vs. Google Pixel 2 XL: Can Huawei top the Android powerhouse?
Huawei has finally taken the wraps off the Mate 10 and Mate 10 Pro. These devices boast top-tier specs, beautifully large displays, and a design that puts them firmly in the 2017 flagship category.
Of course, the Huawei Mate 10 and Huawei Mate 10 Pro are not the only flagship phones to recently come out. Google unveiled the Pixel 2 and Pixel 2 XL to much fanfare at its recent hardware event, and they could pose quite a threat to the newly launched Mate 10 series. How does the larger Huawei Mate 10 Pro compare to the Google Pixel 2 XL? We put the two phones head-to-head to find out.
It's clear that both of these phones are powerful devices, but they take a different approach. While the Google Pixel 2 XL offers the Qualcomm Snapdragon 835 processor, Qualcomm's latest and greatest chip, the Huawei Mate 10 Pro features Huawei's latest chip — the Kirin 970, which is coupled with the Kirin Neural Processing Network.
We don't yet know exactly how well the Kirin 970 performs against the likes of the Snapdragon 835, but the fact is that both chips are powerful and both should be able to handle everything you can throw at them right now.
While the 64GB model of the Mate 10 Pro has 4GB of RAM, just like the Pixel 2 XL, Huawei also offers a hefty 6GB of RAM in the 128GB version. It's debatable how much RAM your smartphone needs, but multitaskers looking for a high-performing device might prefer to go for the Mate 10 Pro over the Pixel 2 XL. You can also get a version of the Pixel 2 XL with 128GB of storage, but it still has 4GB of RAM. Neither phone has a MicroSD card slot.
Both of these phones are top-performing phones, but without benchmark results for the Mate 10 Pro, we don't know which one performs better just yet. Right now, this one's a tie.
Both the Google Pixel 2 XL and the Huawei Mate 10 Pro feature some very 2017 design elements. The Pixel 2 XL, for example, has a large display that features beautifully rounded corners and only tiny bezels. Similarly, while the Huawei Mate 10 Pro doesn't have rounded corners, it does have a nice big display worthy of a 2017 flagship. The Pixel 2 XL continues the previous Pixel's trend of offering a mixture of glass and metal on the back — unlike the Mate 10 Pro, which sticks with solely glass on the back.
Speaking of the display, both phones offer pretty nice ones — but the Pixel 2 XL slightly edges out the Mate 10 Pro. That's because it offers a much sharper 2880 x 1440 resolution display over the Mate 10 Pro's 2160 x 1080 pixel display. Both displays come in at 6-inches, and the Pixel 2 XL has a pOLED display, while Huawei has opted for an AMOLED display.
The Huawei Mate 10 Pro offers a fully glass back, while the Google Pixel 2 XL only has a little glass on the back. That could be a big deal for durability, especially if you're prone to dropping your phone, but we'd still advise getting a case. As far as water goes, both phones come in with an IP67 rating, which means they can handle rain and even submersion in water up to one meter deep for up to 30 minutes.
Because of the better display, Google's Pixel 2 XL wins this category.
The batteries on these two phones are actually pretty different. While the Google Pixel 2 XL comes in with a pretty big 3,520mAh battery, Huawei takes things a step further with a larger 4,000mAh battery. That's a big difference. The bigger battery should go a longer way too, thanks to the lower resolution display.
Both of the phones have fast charging of some kind, with the Google Pixel 2 XL able to charge up to 7 hours in 15 minutes, and the Huawei Mate 10 Pro able to charge up to 58 percent in 30 minutes.
Because of the much bigger battery, this one has to go to the Huawei Mate 10 Pro.
When Google unveiled the Pixel 2 XL, it highlighted the record DxOMark score, hailing it as the best camera in a smartphone ever. That's a big deal, and while it's likely that title will be passed around a lot in the next few years, the fact still remains that right now, the Pixel 2 XL takes the best photos.
Still, the Huawei Mate 10 Pro does offer a few advantages. For starters, unlike the Pixel 2 XL, it has a true dual-lens camera. There's a 20-megapixel monochrome camera alongside a 12-megapixel RGB camera, while the Pixel 2 XL only has a single 12.2-megapixel camera. The ability to take two shots simultaneously and merge them allows all sorts of camera cleverness, but Google is able to achieve some similar effects, like bokeh, through machine learning and its so-called "dual-pixel" technology. On the Mate 10 Pro both lenses have a dual f/1.6 aperture — which should be super helpful in low-light situations. The Pixel 2 XL camera has a slightly smaller f/1.8 aperture.
Both phones have an 8-megapixel front-facing camera.
We'll have to conduct more testing to see which camera is truly better, but initially it seems that they're just better for different situations. For now, this one's a tie.
While the Google Pixel 2 XL offers a near stock Android experience, Huawei has done some tweaking to Android for EMUI. The Huawei Mate 9 offered EMUI 5.1, but instead of upgrading to EMUI 6.0, Huawei has jumped to EMUI 8.0 for the Mate 10 and Mate 10 Pro — in an effort to bring the software more in line with Google's latest platform release, which is currently Android 8.0 Oreo.
EMUI offers various tricks and is tweaked to promote Huawei's software and take more advantage of the edge-to-edge display. Whether that's a good thing depends on your tastes, but there are some compelling advantages to stock Android, not least the fact that the Pixel 2 XL will get updated features and security patches immediately as Google rolls them out.
Because of its simplicity, quick updates, and overall lack of bloatware, we're giving Google the win here.
The Google Pixel 2 XL was first announced on October 4, and it's currently available from the Google Store — though you may have to wait a while considering the stock issues that Google has been having. The device starts at $849, which is pretty pricey — but we don't know yet if it'll be more or less expensive than the Huawei Mate 10 Pro in the United States. All we do know about the Mate 10 Pro's pricing is that it comes in at 800 euros in Europe — which is around $945 right now — though that's not necessarily an indicator of how much it'll go for in the U.S.
The Pixel 2 XL is also available from Verizon. We're not yet sure if any American carrier will offer the Mate 10 Pro. For now, this one's a tie — though that could change.
This one was a very close call. While the Huawei Mate 10 Pro is certainly a promising phone — especially in performance, thanks to its NPU, and in battery life, thanks to its massive battery. Those things still aren't enough to beat the Pixel 2 XL's better display, arguably easier-to-use software, and what we expect to be better camera.
Both phones are excellent, and we'll have to wait and see what the price point of the Mate 10 Pro ends up being — but for now, if you're looking for a great flagship phone, we recommend going for the Google Pixel 2 XL.


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A Bible Chapter for Same-sex Attracted Christians
Andrew explains why he thinks 1 Thessalonians 4 is a Bible chapter that can be particularly helpful to same-sex attracted Christians.
Teaching Purposeful Sexuality
Ed shares how his family of churches recently took time to engage with the topic of sexuality.
Power in the Mundane
Andrew reflects on the importance of domestic intimacy.
What's Your Picture of Jesus?
Andy Robinson | 11 months ago |
Andy explains why having an accurate understanding of Jesus is so important when it comes to handling our sexuality.
Something for Youth Leaders
Andrew shares two podcast episodes for youth leaders, one on sexuality and the other on gender identity.
Principles for Reaching the LGBTQI+ Community
Ed offers some principles to challenge the common assumption that LGBTQI+ people are unlikely to want to be introduced to Jesus.
Am I Incomplete?
Andrew considers whether those who are single are incomplete.
Recognising Relational Responsibilities
Andrew shares an important lesson he's learnt and hopes to put into practice in 2022.
Podcast Series: Explore Questions
In our second podcast series, we discuss the eight big-picture questions that we get asked most often.
Podcast Series: Meet the Authors
The first series of the Living Out podcast, 'Meet the Authors', featured interviews with some of those who have written for our website.


Discussion in 'ECU's, Electronics, and Tuning' started by calvin, Oct 9, 2002.
Witch one is the best do you tink???
I will be using it in a p28 for my mini-me setup.
Get the GREDDY KIT and BOOST that shizzy yo!
Let me tell you what I do know about the P28 chips.
If you are using a JDM P28, then the chips listed for D15B will work fine, but if you are using a USDM P28, they will not, unfortunately.
So that limits your choices quite a bit.
Oh, I also saw your other thread on soldering the ecu board, and all the stock chips are soldered directly to the board. There is no factory honda ecu that I know of that comes socketed. So you will have to solder any ECU you get, unless someone has already done it.
Now, on to your chip choices.
What else have you done to the engine, besides putting in the VTEC head. Also, are you using the 1.5 block or 1.6 block?
You don't really want to raise the redline, unless the engine can take it, otherwise, you may do some damage.
If you want to chat some more about this, PM me. I'll be glad to help you out with your choices.
just run a regular P28. no chip needed.
get a vtec controller or hondata instead if you want, but again not necessary.


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Tag: fishingvillage
Mevagissey
There is a small traditional fishing town in Cornwall called Mevagissey. I don't know why but my mind meanders into its narrow steep streets that wrap themselves around tiny old cottages of cob and slate, maybe because it is a lovely sunny day here, and the waters of the Hudson are that calming shade of cerulean that makes you think of all things sprightly. In Mevagissey, Adi and I met a pasty lover. An English Cocker Spaniel who after bathing in the waters on a bright spring day filled with sunshine had pattered in with a pasty in his mouth, looking quite so solemn. He brought humour to that musty shop we were in, brimming with old camping junk and odd ends, old compasses, rusted lanterns, war memorabilia, grouchy old man behind the till.
Mevagissey named after two Irish saints is a modest place where you trudge up a maze of streets that taper up and down, past boutiques, cafés and chip shops. Locals still make their living from fishing, carrying on the legacy of fishing that has been part of its history like Looe which eked out a living from pilchards and smuggling. Pilchard was its backbone to the extent that pilchard oil lent electricity to Mevagissey which happened to be one of the first among the villages in the county to be thus powered up.
The surprise waiting for me in the village was a 18th century building on the harbour that turned out to be a small (and free) museum. A long time ago in that building — the roofs of which were constructed out of beams acquired from smugglers — they would have made boats for smuggling and repaired them. The passage of time has lent it a more sober personality as a museum where it documents life as it would have been in the village in times bygone. You tend to gawp at a different mode of life, a more simplistic one that you would have probably read about or imagined. Great oak beams, a big hearth that would have been warm once, cloam oven and butter churn, barley thresher and cider press. Trappings of another age and time. Oh and how delighted was I to find out that I was in the village that was home to the founder of Pears – you know that oval glycerine soap we all grew up with.
The harbour on which the museum stands is the nerve centre of all action. From it the aforementioned narrow alleys radiate into cliffs hugged by the rows of cosy cottages. Now, drama unrolls with great lucidity before the eyes if you find yourself on the harbour. Courting couples, fathers dealing with tantrums of lads aiming to challenge fearless gulls strutting around for a nibble of your meal please, families sitting along the edges of the harbour with their large polystyrene boxes stuffed with fish and chips, the motley crew of sail boats waiting patiently in the inner harbour.
The end result of the tootling around Mevagissey is that your appetite works itself up, gunning for a huge pasty or fish and chips. You know which it would be. I would peg it on peer pressure (all those people dipping into the contents of their boxes) and a heady mix of aromas wafting out of the doors of the chip shop. For along with the salty smell of the sea hanging thick in the air, you have to cope with those whiffs, or just capitulate. The tang of vinegar and lingering notes of fish frying. Surely you can smell it…
Portloe
Atop the cliffs of the Roseland Peninsula, I sit on the ledge with the wind in my hair and the Celtic Sea below me. Had I taken a few hefty steps back in time to let's say the mid 1800s, I would have peered and spied upon smugglers at work. And wondered if I might claim a share of their loot of French brandy. That is the kind of contraband these smugglers – who doubled up as fishermen – stored in the cellars of their farmhouses in the village of Portloe.
The scene was serious here on the Roseland Peninsula so much so that Customs had to maintain a strict watch here. I like the ring of the name Portloe which seems to have been a variation of the Cornish word, Port Logh, meaning 'cove pool'. When you climb down the steep cliffs and reach the valleys, you see the protected cove that gave it its name and you will see how it is one of the most charming villages in Cornwall. Once a busy pilchard fishing village, it has now been reduced to a quiet one where just about three boats work the seas, returning with a haul of crabs and lobster.
We sat in the old Lugger Inn for a while before we set out on the coastal walk to Portholland. The bummer is that we did not complete the walk because we were a bit late for it, so we quit after halfway up (even this took the better part of an hour with some steep stretches thrown in). Someday we shall do the entire stretch and more.
But the walk just gives and gives.
If you are singing aloud thinking there is no one to hear your hollering, apart from the husband who signed up for it when he married you, look out for the old lady sitting quietly around the bend on a bench hidden from plain sight by overgrown foliage.
Besides the occasional old lady, we saw a man fishing from his perch upon one of the cliffs that meet the shore at some point, and possibly his partner resting on the rocks above him. It made for a peaceful picture.
You can well imagine then that Portloe remains one of my favourite hideouts in Cornwall. I visit it often in my mind's eye when I crave for the sight of those blue waters, the feel of the wind in my hair and some solitude. For there, you can wander lonely as a cloud.
The protected cove in the village
The thoughtful man in the cove
If you are not staying at the Lugger Inn, do drop by for a coffee or even a lovely high tea with Champagne. But that view makes anything taste like Champagne. Even cappuccino.
In his elements. Going down steep edges of cliffs.
The beautiful stone cottages of Portloe. Give me a room in one of them and I will never want for anything again.
Flowery meadows
Those blue and yellow-paned cottages can be rented for holidays
Views on the coastal walk
I would go for such an epitaph. Would you?
The mermaid keeps a watch upon the shore
Lugger Inn (www.luggerhotel.co.uk). A room at this 17th century inn will cost you upwards of £147 per night. But it has gorgeous views and a restaurant where you can tuck into the local produce.
Cove Cottage. You can book this beautiful cottage with the blue panes by the harbour through Cornwalls Cottages (www.cornwallscottages.co.uk). Prices start at roughly £110 per night for the three-bedroom cottage. But they are booked chock-a-block, so consider booking in advance. The early bird here catches the proverbial worm and what a worm this is.
Circular walk from Pendower Beach to Veryan. The story goes that you will pass a spot in Narehead where lived a fisherman, not quite happy in his marriage. Once a week he used to lower his boat into the waters to visit his wife in Veryan and take her a booty of fish.
Walk the coastal path to the cove at Portholland.
Circular walk from Narehead to Portloe. On the path you will spot a Cold War nuclear bunker and a reef called The Whelps because many a ship met its untimely end there.
Sea, Salts and Sail in Mousehole
In the fishing village of Mousehole in West Cornwall which falls understandably within the Cornish area of outstanding natural beauty (AONB) zone, a maritime festival takes place every two years from which I have culled the title for this post. A photographer called Paul Massey (poor thing shares his name with an English criminal gunned down in 2015) describes it as an incomparable experience. He notes: 'To watch as the harbour slowly fills with wooden boats is almost akin to time travel. It reminds me of the old sepia postcards showing the Mousehole fleet of fishing luggers lying abreast, hauling canvas and pulling on cordage. The sights, sounds, and smells all mingling to evoke a very different and romantic era. It is a photographer's dream. When the boats leave to race in the Bay, with St Michael's Mount in the near distance, it sends a shock of excitement through even the most hardened land lubber.'
As we entered the village I wondered if foxes strutted about its narrow streets. One of the house owners is supposed to have sighted a fox cub family and named her cottage accordingly. Cottages built with local granite huddled around the alleys. Flowery gardens and rustic garden sheds popped up alongside, and to my delight, pasty shops and galleries too. One of the shops sold candles which promised to make your room smell of the sea. Then came the part of looking at the price tag which succeeded in making the whiff a shade less potent.
Families sunned themselves on the beach and children went about their serious tasks of building sand castles while girls with pigtails were told off by their fathers in a serious grown-up voice about something or the other – no baby talk here. The village seemed to be protected from the onslaught of the sea by two sturdy breakwaters that popped up on the harbour as a clutch of boats floated upon shallow waters that gleamed in jewel tones of turquoise.
Mousehole – pronounced 'Mowzel' please – with the distinct lack of crowds made us feel like it was our personal romping grounds. Did the poet Dylan Thomas comment therefore that it was the loveliest English village? I am sure the Cornish might have had a thing or two to say about being called English.
But do not be fooled by its present unassuming self. Mousehole was once a port of distinction along with nearby Marazion. This was until the 16th century when a marauding band of 400 Spanish men razed the village to the ground – the backdrop to this was the Anglo-Spanish war of 1585-1604. Only one house remained standing and that was the pub, the former Keigwin Arms, which remains but is no longer a pub. On it is a plaque that informs you that 'Squire Jenkyn Keigwin was killed here 23rd July 1595 defending this house against the Spaniards.'
Now if you are into Stargazy Pie, that Cornish dish which has seven fish heads poking their heads out of the crust to say hello to the eater, this is its birthplace. Every year on December 23, an enormous Stargazy Pie is baked to honour the memory of a local resident, Tom Bawcock, who braved the stormy seas and rescued the village from famine by returning with a haul of seven kinds of fish. Below you shall find examples of its charm.
Breakwater security
Looks like we have a Daphne du Maurier fan in the house
Mr. Personality
Whitewashed cottages of Mousehole. I have a decided weakness for the colour scheme.
Lichen-coated harbour of Mousehole
Gulls ponder upon the unfairness of life as people with pasties pass by


Senior IT with ability to deliver bottom-line results while formulating and implementing advanced technology and business solutions. Excellent communication and leadership abilities.
Oversee architecture for IT solution projects from conception to completion.
· Directed 25 technical personnel to deliver a major IT infrastructure replacement project.
· Designed an IT infrastructure solution to reduce hardware support and maintenance requirements.
· Provided expert IT solutions to address clients' business needs.
Directed team to provide design and implementation operations for a data warehouse. Used delegation skills to ensure project was completed in a timely and accurate manner.
· Created data warehouse which allowed accurate daily sales reporting and forecasting.
Provided consultations to formalize and implement methodology for corporate data warehousing solutions.
· Reduced support and maintenance reauirements by 30%.
· Worked with stakeholders to centralize vital data and reduce redundancies.
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