Suitable Locations for Grocery Stores in …...Suitable Locations for Grocery Stores in Underserved Areas, Rochester, NY Jaclyn Pryll, CRP 386 – Fall 2008 5 block group level. I

Jaclyn Pryll

CRP 386: Intro to GIS

School of Architecture

University of Texas at Austin

Fall 2008

Suitable Locations for Grocery Stores in

Underserved Areas, Rochester, NY

Suitable Locations for Grocery Stores in Underserved Areas, Rochester, NY

Jaclyn Pryll, CRP 386 – Fall 2008

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EXECUTIVE SUMMARY

There are areas in the city limits of Rochester, NY, that are underserved by supermarkets

and grocery stores that offer healthy and fresh foods. For several decades, a trend exhibited by

urban grocery stores nationwide is their continuation to close down stores only to relocate or

development new stores in the suburbs. For the purpose of this report, the focus is on medium to

larger retail grocery stores (around 40,000 square feet) to use to identify where there are

locations in urban areas of Rochester, NY, to offer underserved areas better varieties of fresh

foods. This research uses GIS to evaluate the demographic characteristics of census block

groups to identify areas lacking grocery store locations as well as identify areas in need spatially.

The results will provide parcel candidates for new store locations based on a ranking of available

vacant parcels and their locations within certain demographic and spatial characteristics of

Rochester. Demographic characteristics chosen, such as household median income, total

population and racial population distribution, are criteria seemingly most influential in the

grocery gap trend, both directly and indirectly. Adding the demographic data sets with

identifiable grocery industry spatial standards such as location proximity to major streets, current

store locations, and public transit attempt to use standardize criteria to appeal to the practicality

of actually locating a new store in underserved areas. Hopefully, the sites chosen could be

considered good candidates for sites sought by the grocery-chain industry for future

development. The criteria used to locate suitable parcels does not, however, provide concrete

criteria used in every grocery store-chains methodology for what fits individual company goals,

but it does provide basic economic and spatial factors that appeal to any profit-making

corporation. The results provided can be used both by the grocery industry as well as local

policy makers. Grocery store companies can see that success can be possible locating back in

the city limits, and local policy makers can formulate policies to ensure hardships aren’t

increased on those underserved with healthy food choices because of the ineffective policies

currently in place that do not ensure proximity to retail food stores.



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INTRODUCTION

The trend exhibited over the past 40 years by grocery stores and supermarkets closing

stores in urban and inner-city areas in favor of locating in suburban neighborhoods is called the

“grocery gap”. This can be considered a by product of “white flight”, the movement of the white

inner-city population to the suburban areas where often the higher paying jobs were locating

and/or followed. The abandonment of the urban areas for lush suburban pastures left many in its

wake such as lower income and minority populations unable to move to or afford a higher cost of

living. Amongst the sprawled environment also came the mega-sized supermarkets that were

able to expand their store footprint from 25,000 square feet to up to 70,000 square feet based

much on available land that also cost less to lease or purchase than many urban properties. The

City of Rochester, NY, has been no exception to the “grocery gap” trend. In the upstate NY

region, several grocery store companies compete for business, but one grocery store chain has

served Rochester for many decades and has also been the largest exhibitor of relocating from the

city to the suburbs. Though this paper isn’t aimed at placing certain companies in the spot light

for their business decisions to not locate in the urban areas of Rochester, their choices to relocate

to the suburbs have left many people underserved by market places offering healthy foods at

competing prices.

Grocery stores, for this paper, are defined as stores ranging from 30,000 to 70,000 square

feet and offering fresh produce, a meat and dairy department, and a grocery area. Wegmans is a

successful grocery store chain that has its birthplace in Rochester and has served Rochesterians

for decades. From its conception, Wegmans has provided Rochesterians with fresh produce,

baked goods, fresh seafood, meat, deli products, and international foods. As the company and its

success grew, so did the store size and its offerings of merchandise of 70,000 items, compared to

40,000 for most supermarkets. Assessing that locating on the city fringe and outside of it has

more benefits than not, Wegmans has closed many inner city stores. The City of Rochester’s

local government and area non-profit organizations (NPOs) have successfully recruited other

grocery retailers to locate and open grocery stores in several urban areas in Rochester, but there

are still areas very underserved and in need of healthy food choices. Currently, Wegmans, Tops

Friendly Markets, and PriceRite Supermarkets are the three grocery store retailers operating in

Rochester. In some of Wegman’s abandoned stores, Tops and PriceRite have opened stores in

the Rochester area.

Inner-city neighborhoods are often times higher concentrations of minorities, lower

income households, and dependant on public transit. Increased distance to retailers of healthy

foods causes a strain on these already struggling households, and lack of healthy choices can

contribute to unhealthy diets and increased risk of dietary diseases. These kinds of consequences

may not be a direct responsibility of the retailer but food is a basic necessity, and as a food

supplier, it seems irresponsible to abandon areas in need of this basic food resource. Rochester is

the county seat for Monroe County in NY state. Within the boundaries of Rochester, there are

seven grocery stores serving 219,026 people (2000 census), and outside the city boundaries but

within Monroe County there are 21 stores serving 516,317 people (2000 census). The ratio of

grocery store per persons in the city is 1:31289, whereas in the suburban and rural areas the ratio

is 1:24,586.

The location of grocery stores by grocery store companies depend on their own market

analysis and business strategies, but the standard industry assumptions for locations depend on

the average volume of traffic on roadways in front of proposed sites, placement at intersections



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of two major thoroughfares, visibility of the site from the road, and the political and business

climate of the community (Nienow, MEMO). Household income becomes a factor when

considering “stores try to locate in areas where they will generate between $350 and $500 of

business annually for every square foot of building space,” (Nienow, MEMO). A 40,000 square

foot building would, then, need to generate an estimated $17 million in sales annually.

PROBLEM STATEMENT

It is necessary to assess the climate and conditions of Rochester to see if there is indeed a

trend exhibited of the “grocery gap”, and if it can be identified, than to provide choices for the

people of Rochester as well as the grocery industry of places to locate a new store in areas in

need. The urban core has become a cliché in rust belt cities such as Rochester of poverty and

physical abandonment. Though Rochester’s population hasn’t increased much over the past

several decades there is still a large population deserving of healthy food choices closer than are

currently available. If it can be shown that there are indeed suitable parcels, then the issue can be

made more of a priority and can also be give more substantiality as an issue needing to be

addressed by the grocery industry as well as local officials.

RESEARCH QUESTIONS

The primary research question is: “Where are suitable locations for new urban stores in

underserved areas in Rochester, NY?” Questions that arise from this primary question are what

areas are underserved, and can grocery stores find locations in underserved areas using their own

standards and criteria? If suitable locations are found, does it support the need for planning and

public policy to better ensure that communities in need of healthy food choices are provided with

closer locations to this basic necessity?

METHODOLOGY

Data Collection

The primary source of spatial data was obtained through the Monroe County Department

of Environmental Services. For $15, a CD was purchased with shapefile data that pertained to

the area of Monroe County (which included information for the City of Rochester). The City of

Rochester didn’t have shapefile information readily available for download, and purchasing the

CD, especially since it had all pertinent layers for my study area, seemed the most viable. The

shapefile layers I used were parcel data, Monroe County boundary limits, City of Rochester

boundary limits, and street centerlines (though there were several additional shapefiles on the CD

that I didn’t think would help with my analysis).

To obtain bus route shapefiles for the city, I emailed a representative with the Genesee

Transportation Council who, after having received a written request for the shapefiles for

licensing purposes, emailed me back the shapefiles for the bus routes. To obtain demographic

information, The United States Census Bureau’s 2000 Census Summary File 3 contained

information regarding total population count, population count based on race, and median

income for households within the Monroe County area. The SF-3 file is given at the census



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block group level. I felt that this level was fine for my project. I joined the census demographic

statistics with TIGER data shapefiles downloaded from on the US Census Bureau website. Since

the Monroe County GIS shapefile data was projected in NY State Plane (NAD83, survey feet)

FIPS 3103, I identified that as my final map projection and projected the TIGER data and the bus

routes in that same projection. I struggled with providing transportation data such as trying to

find the number of owners of cars per households, and I was unable to find a category that

satisfied me within the SF-3 data. I found “mode of transportation used when going to work”,

and “private vehicle occupancy for workers over 16”, but neither seemed suitable for my type of

analysis. I chose to not use census transportation data as I was not focusing on accessibility to

stores as much as locating stores in areas that were without one.

Being from Rochester, NY, I am familiar with the grocery store companies in Rochester

that fit the type of store I was interested in locating parcels for, so to obtain their addresses I used

the internet to search for their store locations on the store’s individual websites. I copied down

the addresses on an excel sheet for all those located within Monroe County. At first I had

thought I would code the different companies individually, but then decided that it wasn’t as

important to highlight which companies located where, but just to look at them as industry

representatives and to code them all the same.

I referenced several articles on the “grocery gap” as well as other articles discussing the

lack of grocery stores in poorer neighborhoods. Data found in articles regarding the need to

locate in more underserved urban areas provided a foundation for my research question and

problem statement. It also provided the “assumed industry standards for store locations” that I

used to chose my data sets for my suitability analysis.

Data formulation and modifications

It was important to clean, “prep”, and formulate new shapefiles in order to run the

analyses. This included a number of processes to be performed:

Geocode List of Current Store Locations Addresses

I geocoded the list of grocery store addresses both automatically and interactively.

After I created an excel sheet of the 28 addresses of all grocery stores in Monroe County,

and then performed a batch conversion, my success rating of matched addresses came

back as 22 matched addresses and 6 unmatched addresses. Three of the addresses needed

the directional moved from the “suffix” box to the “prefix” box. One address needed the

zip code changed. One address needed 4th

to be spelled out as fourth, and the final

address needing a fix was regarding the format of the street name. The input name was

actually two streets that crossed near the store. Following a google search of the exact

street address, I was able to format it correctly. This all produced 100% matches.

Creating Rochester City limits shapefile

Since the City of Rochester was to be used as my study area, it was necessary to

create a shapefile that provided the city boundary for other data to be clipped to. The

Monroe County GIS data included a line and polygon layer called town_villages. I

selected the polygon for Rochester and exported it out as a new layer.

Clipping data to Rochester City Limits shapefile

Using the Rochester city limits shapefile, I clipped other shapefiles to be used for

the analyses to the city limits shapefile. These layers were the TIGER data shapefiles for



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the census information, street centerlines, and parcel shapefiles. I exported out the major

streets in the city of Rochester based on their ROADTYPE classification as divided

highway or state route, and created a seperate shapefile representing major streets.

Joining census tables to TIGER data

My excel documents needed to be in the 97-03 format to be compatible with the

shapefiles. After downloading my census data, I removed the second row that were

column descriptions, renamed the file, and used those renamed files to join to my TIGER

data shapefiles. I downloaded the total population and racial population in one table, and

the median income data in another table.

Creating new TIGER and census data layers

I exported out TIGER data profiling certain census data as separate layers to use

in different maps. For the TIGER shapefiles containing income data, I chose the field

value for median income on the joined census table. I applied the Natural Breaks

classification with 5 classes. The natural breaks method emphasized the lower income

brackets. I felt the information became distorted using the equal interval or quantile

classifications because the data was composed mostly of incomes below $50,000, yet

since there were income brackets reaching $100,000, the data appeared flat, or without

variation as all the lower income were squished into one or two classes. One thing to

note is that when I used the city limits as my extent in creating a median income shapefile

for the city, it only included income levels up to $100,000. When I created a countywide

shapefile with income brackets, though I used the natural breaks with 5 classes, the

highest income level was $130,000. They, therefore, aren’t used to compare to each

other bracket by bracket, but more as an over view of income distribution from two

different scales.

For the total population and racial population shapefiles, I first created a column

in the excel file for acreages per census block group in the TIGER data shapefile. Then, I

joined the census table to the TIGER data. For total population, I used the natural breaks

classification with 5 classes. I normalized it with acre. This created a population

distribution based on census block groups. For the racial population shapefiles, I used the

natural breaks classification with 5 classes as well, but I used the individual racial

population fields and normalized them with the total population field to give a percentage

of the races that are located within the block groups.

Using buffers to show walkability and households with access to stores outside the city

boundary

I created a ¼ mile buffer to place around existing stores to show the capture area

of population that could potentially walk to the stores for their grocery needs as opposed

to relying on cars or public transportation. I also created a ¼ mile buffer around the city

limits to capture any stores outside the city limits that theoretically are within

accessibility of the city population. When applying the city limits buffer to capture

outlying store locations, I found no stores that were located within it that would be

included in the study for the city population. Therefore, I did not include the buffer in

my analysis maps.

Selecting vacant parcels for the suitability analysis

Selecting vacant commercial parcels required using the attribute table associated

with the parcel data. The field PROP_NBR contained numerical coding, and the field



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next to PROP_NBR, PROP_DESC described the properties usage. I obtained a pdf file

of the 2003 zoning map for Rochester, and I georeferenced the pdf (reformatted as a jpg.)

into ArcMap using the parcel layer shapefile that was clipped to the city limits boundary

as my guide. I noted that the parcel PROP_NBR field was in a format where the first

digit more or less correlated with a zoning category. I used the “select by attribute”

function to distinguish the land use categories so I could create the parcel land use map as

well as find the vacant commercial parcels. I also selected parcels that were 2.75 acres or

more. That size lot could support a 40,000 square foot store, a parking lot, and any other

room needed to comply with local building regulations such as impervious cover

requirements. I exported out the PROP_NBR value for vacant commercial parcels and

the acreage as a separate layer to use in the suitability analysis.

Setting up the Suitability Analysis

The suitability analysis required some critical thinking as far as criteria and data sets used

to perform the analysis. I decided I wanted to show outcomes for suitable parcel rankings based

on two different perspectives: the grocery store perspective and the perspective of those in need

such as lower income and dependent on public transit. Both perspectives used the same data sets

taken from the industry standard assumptions mentioned in the MEMO written about store

locations in Cary, NC as well as my own data sets based on other articles I felt relevant to the

study. They were: vacant commercial parcels, major streets, bus routes, existing store locations,

and median income levels. The reclassifying of income data was catered for each perspective as

well as different weights were given for each perspective. All classifications were using equal

interval and were split into 10 classes so they could be compared to each other.

To perform the suitability analysis, the data sets (major streets, bus routes, other stores,

and income) vectors were formatted into rasters, the rasters were then reclassified, and then they

were ranked and formatted back into vectors. Distance in close proximity to major streets, bus

routes, and distance away from other stores remained consistent within the reclassifications. The

income reclassifications were modified for each perspective, though. Weights were catered to the

perspective of the analysis. The vacant commercial parcel layer was intersected with the ranked

data sets to produce the ranked parcels.

Suitability analysis according to grocery store criteria

Reclassification of Income was determined by looking at the locations of stores outside

the city boundary and where they were located with income brackets. I chose this

method to determine reclassification since that’s where stores were mostly located.

Income Bracket number of stores

outside city limits

0 - $13,000 0

$14,000 - $26,000 0

$27,000 - $38,000 1

$39,000 - $51,000 6

$52,000 - $64,000 9

$65,000 - $77,000 4



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$78,000 - $89,000 1

$90,000 - $100,000 0

$110,000 - $120,000 0

$130,000 0

Income Reclassification (10 being the most favorable)

Income bracket ($) reclassification

0 - 10146.1 1

10146.1 - 20292.2 3

20292.2 - 30438.3 5

30438.3 - 40584.4 8

40584.4 - 50730.5 9

50730.5 - 60876.6 10

60876.6 - 71022.7 7

71022.7 - 81168.8 6

81168.8 - 91314.9 4

91314.9 – 101461 2

Weight given to data sets:

Data Set Weight

location within income

bracket area

45%

distance to streets 35%

distance from other stores 10%

distance to bus routes 10%

Suitability analysis according to lower income and public transit perspective

I wanted to reclassify the income brackets differently with more emphasis on

lower income households as well as place more emphasis on bus routes as poorer

households rely more on public transit. I felt locating closer to public transit routes

would help service those underserved areas by making the stores more accessible.

Income Reclassification (10 being the most favorable)

Income bracket ($) reclassification

0 - 10146.1 9

10146.1 - 20292.2 10

20292.2 - 30438.3 8

30438.3 - 40584.4 7

40584.4 - 50730.5 6

50730.5 - 60876.6 5

60876.6 - 71022.7 4

71022.7 - 81168.8 3



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81168.8 - 91314.9 2

91314.9 – 101461 1

Weight given to data sets:

Data Set Weight

location within income

bracket area

45%

distance to streets 10%

distance from other stores 15%

distance to bus routes 30%

FINDINGS (Refer to following maps)



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This map was created to show the regional perceptions in identifying the locations of

grocery stores in Monroe County in comparison to the household income and population

distribution of the county area. Our focus area is Rochester, but this is to show the regional

perspective surrounding the city.



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This map was created to illustrate the locations of the seven grocery stores within

Rochester’s city limits. It highlights store location in relation to total population distribution, and

it also shows a ¼ mile buffered distance around each store to show the population captured in a

walkable area from store proximity.



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This map was created to show the locations of current grocery store locations in relation

to the median income earned per household within the city limits.



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This map was created to show the different racial population distributions in relation to

current grocery store locations within Rochester’s city limits.



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This map was created to show the current land uses for the city landscape. It is not a

zoning map, but it largely coincides with the zoning for the city.



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This map shows the five criteria used to perform a suitability analysis for detecting

suitable parcels for new store locations. These data sets were chosen based on industry standards

and were weighted and ranked to create the following two suitability analysis maps.



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This map was created using the data sets in the map Criteria Used for Suitability

Analysis. It is a ranking of suitable parcels based on weights assumed to most favor the grocery

chain-stores perceptions of successful sites.



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This map was created using the data sets in the map Criteria Used for Suitability

Analysis. It is a ranking of suitable parcels based on weights assumed to most favor the lower

income households and households in proximity to public transit routes.



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ANALYSIS

Showing Rochester exhibits the presence of “the grocery gap” in underserved areas

This was achieved through the County Population and Income and Store Locations, City

Grocery Store Locations and Total Population Distribution, and Median Income map layouts.

County Population and Income and Store Locations set up the regional setting for the City of

Rochester and tried to show the existence of suburban grocery stores exceeding the number of

inner city stores even though the highest concentration of the counties population lies within the

city limits. The median income brackets for the county compared to the total population

distribution show that the largest concentration of people are also in the lower income brackets.

Even though there are twice as many people outside the city limits, there are three times as many

stores. The stores themselves appear to cluster around higher income brackets except within the

city limits. Even though income brackets for this map were used with the same classifications as

the income brackets for maps at the city limits scale, the inclusion of the highest median income

is different per map scale. The county map includes areas where earnings are of higher median

incomes, so the city center appears very poor compared to the rest of the county.

The City Grocery Store Locations and Total Population Distribution map layouts also

emphasize a clustering of grocery stores around lower population distributions as areas of high

concentrations of population are left without a store. Only one area of high population

distribution shows two store locations. Six of the seven stores over all, though, formulate a

linear pattern from the NW corner of the city towards the SE corner. Along this linear path also

shows a linear pattern of population distributions per census block group of 5500 or less. The

SW corner of the city and the NE corners are physically lacking a grocery store to serve them.

The Median Income map layout shows the grocery store locations in relation to the

median income brackets of the city. Three of the seven stores are located in lower income

census block groups, and the remaining four are located close to the census block groups with

the highest median incomes. Three of the four located within the highest median income areas

are also within ½ a mile of the each other. They are clustered in what appears to be the wealthy

part of town based on income. One misleading factor to note is the presence of a high income

area where the land is actually parkland or forest. That is in the upper most northeast area of the

city limits. There are a very few amount of people living in this area, though it appears to be a

largely wealthy area. The size of this census block group is one of the largest, so the median

income for this block group seems rather distorted when looking at the rest of the area.

The Racial Demographics map layout of the city needed more maps or data to

substantiate a claim that the stores are not locating in minority areas, an assumption that is a part

of the criteria of underserved areas. It is important to note, though, that a majority of the stores

are located in highly white populated areas, and the largest percentage of black population within

the city is without a store. Stores are also located near the high concentration of Hispanic

populations, though. Further maps and analysis would need to be performed, though, to fulfill

the notion that minority neighborhoods as being underserved to take my suitability analysis

towards the socioeconomic justice perspective.



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Suitability Analysis – Finding suitable parcels for new stores

I was able to locate suitable parcels for new store locations according to criteria used as

“industry standards” as well as those data sets for areas in lower income that are not considered a

priority with the grocery store industry. I made two suitability analysis maps to show the

difference (if there was to be any) between the suitable location of a new store in Rochester

according to industry standards, and the suitable location of a new store based on standards used

to categorize a neighborhood that is underserved: lower income and public transit users. I did

lack information regarding the areas that are dependent on public transit, and I think if I included

this information, I may have discovered different suitable parcels. As it stood, though, the bus

routes for Rochester appear quite extensive, and if a grocery store were to locate along one of the

routes and there is no current stop, a stop could be created.

Suitable Parcels Based on Industry Standards with Emphasis on High Income and Major

Streets shows the most favorable parcels on the edge of the city, no doubt near higher income

brackets. One of the criteria for reclassifying was that higher income was more favorable, so it

confirms that these areas are those with higher incomes. One parcel is actually right next to the

cluster of existing stores (shown on other maps) that surround the SE part of the city, or the

wealthy area. Locating close to other stores doesn’t appear to be as big a threat as one would

think, either. It seems that stores may bring in different clientele or still make a profit if the area

is wealthy enough and people find all stores appealing.

Suitable Parcels based on Industry Standards with Emphasis on Low Income and Bus

Routes shows one parcel that is in the same area as in the map assumed to follow grocery store

industry standards. This is a promising discovery since two parcel locations next to each other

are suitable according to the grocery industry and the needs of the underserved. The most

promising parcel according to the lower income bracket and near bus routes is actually located in

an area that is of a high black population, lower income, and without a grocery store for about a

one mile radius. It is identified as the 3rd

suitable parcel insert located in the southwest part of

the city. This would be the most ideal parcel to build on should the issue of underserved areas

needs being met ever become a priority to the grocery store industry.



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CONCLUSION

Finding suitable parcels in underserved areas of Rochester, NY, was achieved, but the

study could benefit from further analysis. This kind of analysis can address the problem of

underserved areas in proximity to food retailers more thoroughly if the research question is

expanded beyond simply finding a suitable location. Addressing the grocery gap can be

achieved through accessibility studies as well as focusing on other sources of food suppliers such

as farmer’s markets and local area food banks. This preliminary study doesn’t dig deep enough

into the socioeconomic factors and repercussions of limited access to healthy food sources.

Further analysis into different types of demographic data such as reliance on private

vehicles versus public transit and earned income through work versus public assistance could

shed new light into the depths of this trend as having deeper meaning. Public policy might be

persuaded more if there were enough data supporting the definition of underserved and the

admission of grocery store retailers failing to acknowledge responsibility to provide all income

levels with healthy food choices. On the other hand, reasons why stores don’t locate in areas of

lower income could be supported by crime statistics, inability to capture enough annual revenue

based on the income capture, and/or conditions of the business and social climate.



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REFERENCES AND DATA SOURCES

Articles

Abrose, David M. Retail Grocery Pricing: Inner City, Suburban, and Rural Comparisons. (Jan.,

1979). The Journal of Business, vol. 52, No.1 pp. 95-102.

Free Shuttles Can Close the Grocery Gap. 2003-04-15. University of California: UC Newsroom,

http://www.universityofcalifornia.edu/news/article/5318.

Nienow, Sara. (2003, June 6). Grocery Stores along High House Road – MEMO to Town

Council. Cary, North Carolina.

Pothukuchi, Kameshwari. Attracting Supermarkets to Inner-City Neighborhoods: Economic

Development Outside the Box. Economic Development Quarterly, 2005; 19; 232.

http://edq.sagepub.com/cgi/content/abstract/19/3/232.

Supermarket Access in Low-Income Communities. Prevention Institute:

www.preventioninstitute.org.

Winerup, Michael. (1987, January 20). An Inner City Asks For a Supermarket. New York

Times, New York and Region, Column One: Our Towns.

Data Sources

A) Census Block Groups

•Format: Polygon Shapefile

•Includes: Total population, racial population, and household income.

•Coordinate System: GCS_WGS_1984; D_WGS_1984; Greenwich,

Degree

•Details: This layer originally came from the 2000 Census/ TIGER files, joined with the 2000

SF3 survey information.

•Sources: http://www.census.gov/main/www/cen2000.html,

http://www.esri.com/data/download/

census2000_tigerline/index.html

B) Monroe County Geographical Data

•Format: Line and Polygon Shapefiles

•Includes: Parcel data, street_centerlines, town_villages

•Coordinate System: NY State Plane (NAD83, survey feet) FIPS 3103

•Details: These layers provide county boundary, city boundary, street centerlines, and parcel

data.

•Sources: Monroe County Department of Environmental Services: CD format

C) Bus Routes

•Format: Line Shapefiles

http://www.universityofcalifornia.edu/news/article/5318

http://edq.sagepub.com/cgi/content/abstract/19/3/232

http://www.preventioninstitute.org/

http://www.census.gov/main/www/cen2000.html

http://www.esri.com/data/download/



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•Includes: RTS bus routes


•Details: This layer shows accessibility to/from existing grocery stores in relation to bus transit.

•Sources: Genesee Transportation Council: emailed shape files based on Agreement of

Conditions.

D) Locations of Grocery Stores

•Format: Point Shapefiles

•Includes: georeferenced points of store locations


•Details: This layer shows the current locations of grocery stores.

•Sources: grocery store websites:

Wegmans Food Markets website for store locations: http://www.wegmans.com

TOPS Markets website for store locations: http://www.topsmarkets.com

PriceRite Supermarkets for store locations: http://www.priceritestores.com

http://www.wegmans.com/

http://www.topsmarkets.com/

http://www.priceritestores.com/



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APPENDIX

A) Geocoding grocery store addresses

Existing locations for major retail grocery stores have been inputted into a table.

Open this table in Arc Catalog and drag it into the Arc Map blank document.

Right click on Major Grocery Stores and click geocode.

Import the Street USA (Street Map) address locator.

Use default settings and make sure that zip code is selected in the dialog box. Click

perform address match.

Check the dialog box to determine how many addresses were matched automatically with

80% accuracy and click the Match Interactively to match those addresses that need to be

updated for matching.

Examine each address and correct any spelling errors or typos and hit enter, match all

addresses within 75-80% accuracy.

Rename the new layer and change symbology to correlate with land use.

B) Displaying census data in Rochester

Obtain demographic information such as Summary Files 3 data within Monroe County

from 2000 Census Bureau. Select all block groups within the county. Download SF3

census tables for: Population Density, Racial distribution of population, and Household

income distribution

Download these files and export them to excel

Download TIGER data at census block level to get shapefiles

Download Monroe County street_centerline and town_village shapefiles

Find the two fields with the exact same information in the TIGER data and the census

data. The STFID field will be the join field in the census data to join to the GEO_ID_2

field in the TIGER data.

Use Arc Toolbox to first define the census tract shapefile and Monroe County GIS

shapefile.

Use Arc Toolbox to reproject the shapefiles as NY State Plane (NAD83, survey feet)

FIPS 3103

Begin a new ArcMap project.

Add the census tracts, town_village shapefiles and street_centerline shapefiles to your

map.

Extract the City of Rochester polygon from the town_villages shapefile and create it as a

new layer. This will be the boundary for the study area.

Clip the street layers to the City of Rochester shapefile.

Add the table of SF3 demographic data which was downloaded earlier to my ArcMap

project.

Join this table to the attribute table for the projected block group boundary using STFID

as the common Field



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C) Suitability Analysis

Setting up spatial analysis

add layers to ArcMap: stores, streets (major), bus (routes), vac_comm (vacant

commercial properties over 2.75 acres), city_limits (roch city boundary), and income

(median income census data)

Activate Spatial Analysis

select Spatial Analysis→Options

on the extent tab, set the analysis extent to same as layer “city_limits”.This becomes the

extent for the outputs for every analysis.

Click OK

Raster Analysis

1) Finding straight line distance: Major Streets. The closer the better.

a. In the Spatial Analyst toolbar, navigate to Spatial Analyst Distance Straight

Line to create a raster based surface of distances based on a straight line.

b. Select streets as the layer in the Distance to: field.

c. In the Output raster: field, I saved the output in my Suitability_Analysis

suitability_analyst_store folder and named it streets_dist.

d. Click OK.

2) Reclassify Distance to Major Streets:

a. On the Spatial Analyst toolbar, navigate to Spatial Analyst Reclassify.

b. Specify streets_dist in the Input raster: field.

c. Click on Classify.

d. In the Classification window, make sure that Method: is set to Equal Interval and

Classes: is set to 10. It is important that the classification is set to equal interval

because this is a type of classification that can be kept the same between all of my

different layers. I want them the same so that I can compare the categories.

e. Click OK.

f. In the Reclassify window, I need to invert the New Values to reflect my

preference of the vicinity to major streets. Right now the closest values to the

highway are classified as 1, and the farthest away as 10. I want to reverse these

numbers, by typing in values from 10 to 1 in the New values field, so that those

parcels that are closest to the major highways will get the highest score.

g. Delete the last row that includes no data. To do so, right click on the last row and

select Remove Entries.

h. In the Output raster: field, I saved the output in my suitability_analyst_store

folder and named it st_dist_re. (Raster file names cannot be more than 13

characters or contain any spaces.)

i. Click OK.

j. Remove streets_dist from my table of contents.

k. I changed the color scheme to a gradual rank to visualize the classifications better.



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3) Finding straight line distance: Bus Routes. The closer the better.

a. In the Spatial Analyst toolbar, navigate to Spatial Analyst Distance Straight


b. Select streets as the layer in the Distance to: field.

c. In the Output raster: field, I saved the output in my Suitability_Analysis

suitability_analyst_store folder and named it bus_dist.

d. Click OK.

4) Reclassify Distance to Bus Routes:

a. On the Spatial Analyst toolbar, navigate to Spatial Analyst Reclassify.

b. Specify bus_dist in the Input raster: field.






e. Click OK.

f. In the Reclassify window, I need to invert the New Values to reflect my

preference of the vicinity to major streets. Right now the closest values to the

highway are classified as 1, and the farthest away as 10. I want to reverse these

numbers, by typing in values from 10 to 1 in the New values field, so that those

parcels that are closest to the major highways will get the highest score.



h. In the Output raster: field, I saved the output in my suitability_analyst_store

folder and named it bus_dist_re. (Raster file names cannot be more than 13


i. Click OK.

j. Remove bus_dist from my table of contents.

k. I changed the color scheme to a gradual rank to visualize the classifications better.

5) Finding straight line distance: Stores. The further the better.

e. In the Spatial Analyst toolbar, navigate to Spatial Analyst Distance Straight


f. Select streets as the layer in the Distance to: field.

g. In the Output raster: field, I saved the output in my Suitability_Analysis

suitability_analyst_store folder and named it store_dist.

h. Click OK.

6) Reclassify Distance to Stores:

l. On the Spatial Analyst toolbar, navigate to Spatial Analyst Reclassify.

m. Specify store_dist in the Input raster: field.



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n. Click on Classify.

o. In the Classification window, make sure that Method: is set to Equal Interval and




p. Click OK.

q. I do not need to renumber the current classifications since I feel the further away

the better, and the numbering scheme has already arranged them that way.

r. Delete the last row that includes no data. To do so, right click on the last row and


s. In the Output raster: field, I saved the output in my suitability_analyst_store

folder and named it store_re. (Raster file names cannot be more than 13


t. Click OK.

u. Remove store_dist from my table of contents.

v. I changed the color scheme to a gradual rank to visualize the classifications better.

7) Classifying Income: Demographic data

a. Need to create a field that normalizes median income with census data.

b. Spatial Analyst → Convert → Conversion Tools → Features to Rasters

c. Input feature: income. Field: P053001 (median income field). Output Raster:

Income_Rast.

d. Reclassify: Input raster: Income_Rast.

e. Class: Classification set to Equal Interval, classes set at 10.

f. Click OK

Weighting and Combining Datasets:

1) On the Spatial Analyst toolbar, navigate to Spatial Analyst Raster Calculator

a. Type in an equation that will multiply each raster by the percentage weight I have

given it, then add them all together.

b. Enter in the following equation chosen given the weights I’ve chosen for the analysis

- for the grocery store perspective: income = 45%, streets = 35%, stores = 10%, bus

routes = 10%

- for the lower income and public transit perspective: income = 45%, streets = 15%,

stores = 10%, bus routes = 30%

c. Click Evaluate.

d. A new layer called Calculation will be added to your table of contents. This is

currently a temporary layer, but can be made permanent by right clicking on

Calculation and navigating to Data Make Permanent.

e. In the Make Calculation Permanent window, save the calculation name it weights

f. Click Save.



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g. The calculation is saved to my data folder, but the name in the table of contents will

remain the same. Change the name of the Calculation layer to weights.

h. Remove streets_dist_re, stores_dist_re, income_dist_re, and bus_dist_re from my

table of contents

Reclassifying Weights

1. Now, reclassify weights so that it follows the 1-10 ranking system I have been using with

all of my rasters.

a. On the Spatial Analyst toolbar, navigate to Spatial Analyst Reclassify

b. Specify weights in the Input raster window.



Classes: is set to 10.

e. Click OK.

f. Leave the values in the reclassify window as they are.



h. In the Output raster: field, save the output in your data folder and name it

weights_re. (Raster file names cannot be more than 13 characters or contain any

spaces.)

i. Click OK.

j. Remove weights from your table of contents.

k. Note that the value of 10 for weights_re still reflects the most desirable locations.

Combining the Raster and Vector Layers

1. Convert weights_re into a shapefile

a. On the Spatial Analyst toolbar, navigate to Spatial Analyst Convert Raster to

Features

b. In the Raster to Features window, choose inputs that match those shown in the image

below:

c. Save my Output features in my data folder and name it rank.

d. Click OK.

e. Remove weights_re from your table of contents.

f. Open the attribute table of rank.

g. Note that the GRIDCODE field includes the final ranking of the sites.

2. Intersect rank with suitable_areas

a. Open ArcToolbox .

b. In ArcToolbox, navigate to Analysis Tools Overlay Intersect.

c. In the Input Features field, select rank and suitable_areas.



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d. In the Output Feature Class, save the output in my data folder and name the file

suitable_parcels_ranked.

e. Click OK.

f. Remove rank and suitable_areas from my table of contents.

3. Change symbology of suitable_parcels_ranked in order to make the ranking more legible.

The ranks are contained in the GRIDCODE field.

D. Displaying access from grocery stores by walking (1/4 mile)

Download geocoded existing grocery store shapefiles

Select ArcToolbox – Proximity - Buffer

Input feature: Grocery Store shapefile layer

Output feature class: new shapefile named stores_walk_buff

Distance: 1320 linear feet

Disolve Type: none

click OK

Suitable Locations for Grocery Stores in …...Suitable Locations for Grocery Stores in Underserved Areas, Rochester, NY Jaclyn Pryll, CRP 386 – Fall 2008 5 block group level. I

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