1. Empirical Research Data Agglomeration Study Demand Control City Store Types and Results Type Measure From most to Distribution fit; clustered: antiques, Rogers (1965) Density None Stockholm scalar clothing, furniture, grocery, tobacco liquor From most to least Ljubjana, Distribution fit; clustered: clothing, Rogers (1969) Density None San scalar all stores, food Francisco stores, grocery stores Rogers and Distribution fit; Food stores, ambiguous Density Population Ljubjana Martin (1971) scalar results Regress results on Convenience stores of Lee and Distribution fit; population density (not same chain cluster, Koutsopoulos Density Denver scalar point), demographics; different chains avoid (1976) wear or no results each other Western and Chinese Nearest Hong Kong, grocery stores cluster; neighbor Lee (1979) Point None Phoenix, convenience stores distribution fit; Atlanta random overall, chains scalar each other Regress station density on Nearest households, Lee and neighbor Hong Kong, Point demographics, trips into Gasoline stations cluster Schmidt (1980) distribution fit; Denver area densities (not point); scalar weak or no effects From most to least clustered: shoes, antiques, Fischer and Nearest computers, automobiles, Harrington Point None Baltimore Neighbor clinics, gasoline stations, (1996) video stores, supermarkets, theaters Gasoline stations avoid Regression each other more as Netz and coefficient; Population demographics Los Angeles competitive intensity Taylor (2002) relative within increases (not an absolute type; scalar measure) From most to least Jensen, Average store clustered: motorbikes, Boisson, and Point counts in None Lyon banks, groceries, Larralde (2005) contiguous sites hairdressers, laundries, drugstores, savings banks Birmingham, Nearest Population demographics Picone, Chicago, neighbor scalar on scalar; assume On site alcohol retailers Ridely, and Minneapolis- Point and vector; constant effects on cluster more than off site Zandbergen St. Paul, relative within compared stores for alcohol retailers Oakland, (2009) store type vector Tampa
1. Empirical Research Study Data Type Agglomeration Measure Demand Control City Store Types and Results Rogers (1965) Density Distribution fit; scalar None Stockholm From most to clustered: antiques, clothing, furniture, grocery, tobacco liquor Rogers (1969) Density Distribution fit; scalar None Ljubjana, San Francisco From most to least clustered: clothing, all stores, food stores, grocery stores Rogers and Martin (1971) Density Distribution fit; scalar Population Ljubjana Food stores, ambiguous results Lee and Koutsopoulos (1976) Density Distribution fit; scalar Regress results on population density (not point), demographics; wear or no results Denver Convenience stores of same chain cluster, different chains avoid each other Lee (1979) Point Nearest neighbor distribution fit; scalar None Hong Kong, Phoenix, Atlanta Western and Chinese grocery stores cluster; convenience stores random overall, chains each other Lee and Schmidt (1980) Point Nearest neighbor distribution fit; scalar Regress station density on households, demographics, trips into area densities (not point); weak or no effects Hong Kong, Denver Gasoline stations cluster Fischer and Harrington (1996) Point Nearest Neighbor None Baltimore From most to least clustered: shoes, antiques, computers, automobiles, clinics, gasoline stations, video stores, supermarkets, theaters Netz and Taylor (2002) Regression coefficient; relative within type; scalar Population demographics Los Angeles Gasoline stations avoid each other more as competitive intensity increases (not an absolute measure) Jensen, Boisson, and Larralde (2005) Point Average store counts in contiguous sites None Lyon From most to least clustered: motorbikes, banks, groceries, hairdressers, laundries, drugstores, savings banks Picone, Ridely, and Zandbergen (2009) Point Nearest neighbor scalar and vector; relative within store type Population demographics on scalar; assume constant effects on compared stores for vector Birmingham, Chicago, Minneapolis- St. Paul, Oakland, Tampa On site alcohol retailers cluster more than off site alcohol retailers
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