Urban Habitat Chicago - Community Gardening Analysis

Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 1

Urban Habitat Chicago

Site-Selection Analysis -
Finding Suitable Space for Urban Agriculture Initiatives

Summer 2011

Mike Bularz
Interiors 2870 – Internship - Transfer
Summer 2011
Prof. Cynthia Milota


Table of Contents
Introduction page
Project Description 3
Educational Learning Goals
Project Deliverables
Project Timeline
Methodology 5
Spatial Analysis
Data Mining, Data Design 6
Network-based Analysis 7
Data Manipulation and Queries 8
Aggregating all results into final weighted Spatial Analysis 10
Results 11
Trends observed
Quality of Results, Methodology Re-examined 12
Result Maps 14
Input Parameters Map 14
Analysis Results Map 15
Selected Parcels Map 16
Resources (Works Cited in Document) 17
Appendix (All works and resources used in project) 18
Selected City Parcels 21
A note on selected parcels
Selected Parcels 22
Work Log 37


Mike Bularz
Summer 2011
Urban Habitat Chicago
Site Selection Internship

Finding Suitable Space for Urban Agriculture
Initiatives in Chicago

Project Purpose:
Finding suitable locations for Urban Habitat Chicago, either in the form of leased office space,
shared space, or land available for urban agricultural work. Identify need for community gardens
through identifying food deserts (areas where the population has low access to produce), and
identifying suitable land for these types of initiatives as well, such as unused city-owned land or other
nonprofit or public organizations with suitable land, that could benefit through having fresh food in
their own backyard.
Internship Educational Goals:
− Familiarize self with climate for urban agriculture and similar sustainable intitiatives, as far
as gaining a picture of government programs, nonprofit advocates, urban gardening groups
and events and the affect of their programs on communities in Chicagoland.
− Practice, and enhance location decision making skills through the use of Geographic
Information Systems (GIS) software, JSON API's, online databases public and private,
various government agencies at the municipal, county, and federal level and their publicly
available, or conditionally leased data, as well as other sources such as college subscribed
data services.
− Enhancement of related computer skills through spreadsheet, database, and file conversion
software, web API mashups such as Yahoo Pipes through this process as well.
− Learn commercial real estate terminology that would be encountered in future work / issues
dealing with land, public policy, as well as methods for making locational decisions
Project Deliverables:
The project should result in the completion of a portfolio of potential sites, data derivatives related to
food access, and maps of food deserts accessible by public transit.


Project Progression / Timeline:

Setting a clear timeline of various stages of work to complete the project can be difficult
without knowing what resources would be available to begin with. Data collection consumed a large
portion of time as various statistics, tables, geographic products exist in many different locations on the
web through different entities. A few web portals were consulted for source data / information for
criteria ranging from real estate listings to obesity rates, and not all were useful in the end due to
compatibility or scale (finding or creating data at a micro-level such as census blocks can be difficult or
time consuming). A sizeable portion of time was spent in aggregating different formats so that they
could be compatible, and eventually line up for comparison and analysis. Also, a portion of time was
spent on online training for specific software modules such as one for network-based analysis, which is
explained further in the methodology section.
A general note should be made that the project scope shifted midway throughout the project as
the capabilities (and limitations) of GIS technology were better understood and a more useful
application was found. The project intended to find a more permanent location for the non-profit UHC
became the project to find vacant city land that could be more fruitful as a community garden, which
the creation and maintaining of is one of UHC's primary activities (Glenn).
Another change in the project occurred as more data became available through a revamping of
the City of Chicago data portal (“Chicago's Data Portal 2.0”). Various new data was released towards
the end of the project which aided, and somewhat derailed the timeline for the project. Consultations by
phone or in person with various people that had knowledge that could be beneficial had some effect on
methodology in the project as well.


Methodology and Analysis:
The methodology, or process of getting necessary information together and performing analysis
(among other necessary steps) for this project consisted of a few key components. Network-based
Analysis and Weighted Spatial Analysis make up the majority of the methodology for the project. Some
degree of Database Manipulation and Queries was used as well, to a large extent to make data
compatible, and also to create new products. The following illustrates a general timeline of the
methodologies employed:

Step: Data Mining → Data Cleanup, Database Creation, Modeling and Results and
Manipulation → Queries → Analysis Products
Phase: Data Acquisition and Design Processing for new Information Products

The majority of the steps followed a smooth progression but had to be reworked when new data was
discovered and was able to be incorporated into the project.

Spatial Analysis
A common application of GIS technology is Spatial Analysis. Spatial Analysis is the
aggregation of multiple criteria that have a spatial (locational component) into a compatible and
comparable format and then the manipulation of this into useful information products. This application
is often what differentiates simple map products and viewers as trends and phenomena can be put into a
visual and defined format that aids the decision making process. Spatial Analysis products save time
and work by narrowing down possibilities into most suitable ones ("ArcGIS Spatial Analyst |
Brochures/Whitepapers").
Spatial Analysis often involves the conversion of vector defined locations (points, lines,
polygons representing points of interest such as grocery stores, means of moving around, and defined
boundaries such as census blocks or tracts, respectively) into a grid surface (raster, or collection of
square cells) with values representing the criteria or phenomenon. The conversion of input data
representing criteria such as population density, distribution of grocery stores, distances from public
transit into a common surface format is how a comparison between all of the input information can be
made, and a resulting product produced. Spatial Analysis served as a big portion of the methodology of
this project.


Fig.1: Raster surface representation of phenomena such as community garden distribution and
population distribution
A: NeighborSpace Gardens (points) B: Population by Census Block (polygons)

→ →
Result: Density of communtiy gardens surface Result: Population Density surface

Data Mining, Data Design
Performing the analysis required for the needs of this project consisted not only of determining
what factors to consider, but how to get information representing these factors (data). The process of
getting necessary information (data mining) creating a suitable data design, which is a design and
process for aggregating together multiple datasets into a compatible and comparable format
(Tomlinson, 93-107). In a geographic information system, data design must take into consideration
spatial characteristics of the datasets. For example, data from a USDA study of food deserts was
available only at the county level, which served no purpose for analyzing areas within Chicago. Often
times it is sought to somehow capture various characteristics / parameters at the most mico-level, or
lowest common denominator available.
A grid surface with each cell representing a 10' X 10' area was an original design, but when
seen through to analysis, the results seemed to not accurately portray spatial patterns that were being
looked for (see Figure 2). Some of the combined surfaces in this method received more “points” than
others per cell and didn't seem to prove anything. This was because the factors, such as population
density were being compared too directly with relatively related ones such as access to rapid transit.
A different method was applied afterward: the surfaces derived for community garden locations,
grocery store locations, and population distribution were all interpolated into census blocks. Each
factor was now comparable at a block level. Although the block-level design lost locational accuracy,
trends were more visible, and a more meaningful product resulted from this change in data design after
the initial analysis.


Fig.2: Results of poor and good data design
A: Data Design based on cells B: Data design based on blocks

Result: Poor representation – almost all areas Result: Clear trends visible, suitable pockets
come out suitable other than where there are rivers accentuated, better results

Network-based Analysis
Density, or distribution based analysis was suitable for factors such as population density,
density of grocery stores, and density of community gardens. These surfaces display relative
concentrations of these factors well, but when analyzing access to public transportation, which was
seen as a key parameter in selecting a site that is not only suitable but in-line with sustainability – a
goal of Urban Habitat Chicago. The primary reason for this is that the movement of people is restricted
by streets and this has to be taken into consideration. Rings depicting buffers of 50, 100, 150 feet are
not suitable – a bus stop might be 50 feet away from a person at a given location if they had the ability
to fly over them, or dig underneath, but in reality it might be 74 feet or so by walking on the streets.
This is why Network-based Analysis must be used. Network-based analysis starts by building a
network of traversible nodes and lines connecting these nodes ("Essential Network Analyst
Vocabulary"). The lines represent walkable roads in our case, and the nodes turns between roads. For
more advanced applications such as driving, speed limits and one way streets must be programmed in,
and slopes calculated for mountainous areas (not in Chicago, though). For our purposes, a network that
can be traveled at 3mph (approximate rate of person walking) was created. The analysis then calculates
distances from inputs such as bus or train stops, and outputs a result.


Fig.3: Ring Buffers vs. Network-based Analysis
A: Various distance ring buffers around stops B: Walkable street network-based analysis

Result: “As the crow flies” analysis leading to Result: More accurate, based on travel times
inaccurate results

The network-based analysis method was employed to more accurately look for areas of Chicago
with good access to public transportation. Luckily, data is published by the CTA (Chicago Transit
Authority) in a universal format called the GTFS feed. The GTFS, or General/Google Transit Feed
Specification is a standard for publishing data for public transit agencies so that it can then be plugged
directly into a myriad of applications such as route-planning services, schedules, and mobile
applications (“General Transit Feed Specification”). The data from the CTA Developer portal
conformed dilligently to this standard, for the most part (“GTFS Data Feed | CTA Developer Center").
Several issues arose with the network based analysis when analyzing access to public transit.
The very first results placed most of Chicago as accessible to public transit, the reason for this being
that all bus stop, and CTA trains were used. The bus information had to be taken out of the picture or
ranked. Most of Chicago is well covered with bus stops, but not all of these are served as frequently,
and factoring this into the equation was necessary, and a way to rank the stops. Stops needed to be
ranked and emphasized or de-emphasized more based on these criteria.

Data Manipulation and Queries
Several of the datasets used throughout this project were re-worked to fit together better, but


one of the more intense-reworkings was with the CTA GTFS feed, since a lot of the data had various
relationships. The CTA GTFS feed consists of tables representing:
− Stops: areas where a vehicle stops
− Routes: specific paths traveled by vehicles
− Trips: Trips are a sub-category of routes. There are many trips by more than one vehicle on
a given route, on a give day
− Stop Times: Times a vehicle arrives at a stop, and times it departs (in case there is a long
period between these two)
− Calendar: Two tables, one showing days a route is served, another showing holiday changes
− Frequency: This is supposed to show how often a route is served, and was incomplete
(“CTA GTFS Data Feed”). Frequency was calculated by myself to weigh various stops.
The tables have 1 to 1 and 1 to many relationships, and a preliminary arrangement of these was as
follows:

Fig4: Table relationships (1:M = One to many, 1:1 = One to One, M:1 = Many to One)

After arranging these relationships between the tables, new data was created through the use of
selections and summaries. One example of information derived was the number of stops per hour for
each route, this was done by summarizing stop times by trip number and routes by number of trips, this
gave a count of how many stops per trip per route. This was divided by 24 hours as the CTA data gave
times for a given day. A selection was made of stops that only have night-owl service, as this was one


way to classify more active bus routes. Punishing the stops by how much time was lost versus walking
at 3 mph through simple math was also tried.
Plugging the derivatives into the Network-based Analysis
After attempting to identify very transit-friendly areas, it seemed that where one bus stop was
lacking, another made up for it, and similar results not identifying any specific clusters in the city were
derived. It was decided to simply use train stops and add in metra stations to obtain some transit-
friendly areas. Figure 3B above is a closeup of one of the more clear results that was used in the end.

Aggregating all results into final weighted Spatial Analysis
Finally, derivatives portraying distribution of the population, distribution of grocery stores,
distribution of community gardens, and access to rapid transit could be aggregated in an overlay. An
overlay basically performs raster mathematics: each cell in a surface / raster is added up, averaged, or
subtracted. For instance, one may take an elevation surface, and add on to the sea level to show areas
affected by a 3 foot storm surge, these areas might be multiplied by a binary raster (1 for yes, 0 for no)
of where there are people, this would result in information on where to send rescue crews.
In this case we are saving people from under-nutrition. The basic method in overlaying the 4
derivatives is to convert each of them into a surface. The next step is to rank each cell in values 0 – 9 to
get a set of comparable surfaces. A surface of cells representing distances from grocery stores is
incompatible for subtracting from a population surface which contains cells representing how many
people are estimated for the area. For example: a cell corresponding to x latitude and y longitude has
355 people, is 250 feet from the nearest grocery store, and 18 feet from the Red Line. These values are
incompatible; each cell must be re-classified on a scale of 0-9 in comparison to all of the other cells of
a given surface. Population score 4 + Grocery score 2 + Transit score 9 = 15 / 27 possible points for
that cell, the cell scores 0.55 / 9.
For the purpose of this project, a weighted-overlay is done. This module allows for adding an
emphasis on the various factors / combined surfaces. To obtain the final result, 40% importance was
given for access to rapid transit, 20% for access to grocery stores, 20% for being far from existing
community gardens, and 20% for being in a high population density. Grocery store, community garden
distribution, and population surfaces were created at the census block level, the access to transit surface
was not, to preserve true distances.


Fig5: Overlay of variables – Spatial Analysis
A: Community Garden B: Grocery Store C: Population D: Access to Rapid
Distribution Distribution Distribution Transit

(A x 20% + B x 20% + C x 20% + D x 40%) / Max Score = Result Cell Values
→ See Results section for Result Map

Results
The resulting data emphasized some pockets in the city where urban gardening would be
feasible, and was extrapolated onto points representing city land parcels. The highest scoring parcels
scored 7 / 9, and there were only a few of these, there was a significant amount of parcels with a score
of 6, and a majority score 5. A few others received the low score below 5, none scored less than 3. (See
Figure 6B). Figure 6A shows all land in the city, and how it scored on a block-by-block basis.

Trends Observed
It was not uncommon to see pockets of accessible food deserts on the south side of the city.
Since access to transit was part of the equation, the results may bias towards areas closer to the CBD
(Central Business District – the Loop) as there is generally more accessibility to transit. The scope of
this project focused on not just identifying food deserts, but ones that are accessible by train, and this is
why the bias exists. Another trend was that the South side had higher scores because the West side had
a significant amount of existing community gardens, which were also a factor in this analysis.
An interesting but not pictured trend is the high density of available city-land on the West and
South sides. This may be due to higher foreclosure rates, as these areas have the poorer population of
Chicago. This may be a cause as to why the two graphs in Figure 6 are very similar.


Fig. 6: Results of Analysis in Graphs
A: All land (numbers as percentage of total)

B: City-owned land only (shown as quantity of lots)

Quality of Results, Methodology Re-examined
The methodology used to make informed locational decisions could benefit from potentally
different approaches. Firstly, other reports have found food deserts in a much more meaningful
methods. Mari Galagher's pivotal report on food deserts also analyzed areas based on obesity rates,
death from heart-related problems, among other factors – the correlations between this public health
data and the food deserts are very high, and prove a poignant, grim point. This project focused on


factors such as public transit access, among other considerations explained further in this report, and
the results do not fully address food deserts, rather, good places to start a community garden.
Further, the data used, and not used for this analysis could have been leveraged to produce an
even better result had the constraints of time not been as inhibiting. A midpoint switch of the scope of
the project from looking for office space, to looking for public land put analysis in about a 1 to 1.5
month window.
Crime data, which could have been useful to factor in safety concerns for volunteers was not
leveraged, even though it was obtained, and processed. The crime data was released for the first time
through the city's new FOIA (Freedom of Information Act) portal towards the last weeks of the project,
and the dataset is so immense that it was too difficult to pinpoint any “high crime” areas because of
how much crime actually happens in Chicago (“Crimes”). Details of this are too much to digress in this
report.
Grocery store data, which was processed in a manner that simply acquired any food-based retail
is populated with records for businesses that aren't true sources of nutrition, such as convenience and
liquor stores, corner stores, among other things. A retooling of the method of acquiring this data could
categorize the records (stores) into more useful classes: supermarkets, malls, convenience, etc.
If time had permitted, a network of the city's transportation options could be modeled, and then
used to process the resulting high-scoring parcels for true accessibility. Reversing the model to see how
much of the city could be accessed from each parcel, or better, how much of the population, would
result in an even better analysis. The creation of such a dataset and processing all of this information
could consume from 4-6 weeks, based on my experience from running this project.

Result Maps (following pages)
– Input Parameters Map: Input surfaces of parameters: Access to rapid transit,
population distribution, existing community gardens, grocery store distribution
– Results Map: Results of Analysis, overlaid with all city properties. Refer to
legend for colors representing scores 1-9 from the analysis
– Selected Properties Map: Selected Properties, also overlaid with scores.


Resources
(Works Cited)

Glenn, Anna. Personal Meeting. 22 June 2011.

"Chicago's Data Portal 2.0."Chicago's Data Portal 2.0. City of Chicago. Web. 29 July 2011.
<http://data.cityofchicago.org/>.

Tomlinson, Roger F. "Choose a Logical Data Model."Thinking about GIS: Geographic Information
System Planning for Managers. Redlands, CA: ESRI, 2007. 93-107. Print.

"Essential Network Analyst Vocabulary."Web-based Help | ArcGIS Resource Center. ESRI, 17 Dec.
2010. Web. 29 July 2011. <http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html>.

"General Transit Feed Specification."Google Code. Google. Web. 29 July 2011.
<http://code.google.com/transit/spec/transit_feed_specification.html>.

"GTFS Data Feed | CTA Developer Center." CTA - Developer Center. Chicago Transit Authority. Web.
29 July 2011. <http://www.transitchicago.com/developers/gtfs.aspx>.

"ArcGIS Spatial Analyst | Brochures/Whitepapers." ESRI - The Leader in GIS Software. ESRI. Web. 29
July 2011. <http://www.esri.com/software/arcgis/extensions/spatialanalyst/brochures-
whitepapers.html>.
CTA GTFS Data Feed. Apr.-May 2011. Raw data.
Http://www.transitchicago.com/developers/gtfs.aspx, Web.

"Crimes."City of Chicago | Data Portal. City of Chicago, 29 July 2011. Web. 29 July 2011.
<http://data.cityofchicago.org/Government/Crimes/x2n5-8w5q>.


Appendix
(Bibliography)

Glenn, Anna. Personal Meeting. 22 June 2011.
Brouchard, Lee, and others. Monthly Meeting. 22 June 2011.
Brouchard, Lee, and others. Monthly Meeting. 20 July 2011.
Meetings with staff and their input

"City of Chicago: Geographic Information Systems."City of Chicago | Geographic Information
Systems. City of Chicago. Web. 29 July 2011.

City of Chicago:
– Street Centerlines
– Curblines
– Building footprints
– Census block and tract boundaries (Derivative from U.S. Census Bureau) year 2000
– Census population and demographic derivatives
– Neighborspace community garden locations
– List of city-owned land parcels inventory derivatives
– Metra Station locations
– TIF, Empowerment Zones, Enterprise Zones, Special Service Area boundaries
– CPD Crime and arrest data from last two years

"ERS/USDA Data - Food Availability (Per Capita) Data System."Food Availability (Per Capita)
Data System. U.S. Department of Agriculture. Web. 29 July 2011.
<http://www.ers.usda.gov/Data/FoodConsumption/>.

USDA:
– County level food dessert data


"IDPH Database and Datafile Resource Guide."Illinois Project for Local Assessment of Needs
(IPLAN). Illinois Department of Public Health. Web. 29 July 2011.
<http://app.idph.state.il.us/oehsd/ddrg/public/default.asp>

Illinois CDC nutrition data

"Cook County Government, Illinois - Technology, Bureau of Geographic Information
Systems."Cook County Government. Cook County, Illinois. Web. 29 July 2011.
<http://www.cookcountyil.gov/portal/server.pt/community/technology_bureau_of/287/
geographic_information_systems/605>.

Cook County Assessor Bureau of IT:
– Parcel level viewer of photos, assessed values

"GTFS Data Feed | CTA Developer Center." CTA - Developer Center. Chicago Transit Authority.
Web. 29 July 2011. <http://www.transitchicago.com/developers/gtfs.aspx>.

Google / Chicago Transit Authority:
Google Transit Feed Specification (GTFS) data including train and bus schedules, stop
locations, stop times, trips taken on routes, route destinations, days of service, other tables.
Chicago Transit Authority. Night-owl Service - Summer 2011. Chicago: Chicago Transit
Authority, 2011. Chicago Transit Authority. Web. 29 July 2011.
<http://www.transitchicago.com/assets/1/brochures/nightowl.pdf>.
Chicago Transit Authority:
Night-owl bus service schedules and maps

"Low Access Grocery Areas (LAA)." GIS Mapping: Up to Date Demographics, Population,
Unemployment, Crime and More. Policy Map. Web. 30 July 2011.
<http://www.policymap.com/blog/tag/low-access-grocery-areas-laa/>.

PolicyMap / TRF Mapping Services


"Downloads." CloudMade Downloads. CloudMade. Web. 29 July 2011.
<http://downloads.cloudmade.com/americas/northern_america/united_states/illinois>.

Open Street Map:
Derivatives and file conversion of OSM world files for grocery store locations

"Standard & Poor's - Americas." Standard and Poor's. Standard and Poor's. Web. 29 July 2011.
<http://www.standardandpoors.com/home/en/us>.

Standard and Poor's Industry Data:
Locations of grocery stores private and public (registered with S&P)

"NAICS Guide." Census Bureau Home Page. U.S. Census Bureau. Web. 29 July 2011.
<http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart_code=72>.
U.S. Census Bureau:
NAICS (National Industry Classification System) codes for production of industry (food retail
and wholesale) derivatives

Examining The Impact of Food Deserts on Public Healthj. Rep. Chicago: Mari Gallagher
Research and Consulting Group, 2010. Print.
Mari Gallagher report analyzing food deserts and their impact in Chicago.


Selected City Parcels

A note on selected parcels for the portfolio
The presented available land is just a glimpse of potential land. A bias was present in selecting
parcels closer to the North side of the city, where a majority of Urban Habitat Chicago volunteers not
only live, but are more active in community garden efforts, not only in actual community gardens but
ties to organizations active in events and projects in the same area.
Ways forward...
There are similar organizations tied to their own areas of the city as well, and using GIS
technology to increase awareness of available land opportunities through not only showing food
deserts, but making the information publicly available through the construction of a public-map viewer
would be a step in the right direction. My site-selections were biased to UHC's needs and logistical
capabilities, there might be other organizations that are more active in other endeavors – such as
rehabilitating old buildings, deconstruction, etc. that could find these sites more suitable, and find some
of these sites literally “right up their alley”. The details of this are outside of the scope of this report.

4814 N. Kedzie

Overall Score: 5

Distance To Transit: > 1/4 Mile
Nearest Grocer: Super Food Mart
Nearby Community Gardens? 3
Community Area: Albany Park
Sqft: 18757

Notes:
Concrete surface currently used for parking only. Very large lot size.
Farmer's market potential?

3804 N. Cicero

Overall Score: 6

Distance To Transit: ½ mile
Nearest Grocer: Martin's mini market
Nearby Community Gardens? No
Community Area: Portage Park
Sqft: 3126

Notes:
Concrete surface. Being marketed as “development opportunity” by
city, as pictured. Part of cluster of lots, two concrete, and one grass.

3707 N. Cicero

Overall Score: 6

Nearest Grocer: Martin's Market
Sqft: 3127

Notes:
Part of cluster of city lots. Grass, no use. Potential for community
gardening.

3626 N. Cicero

Overall Score: 6

Nearest Grocer: Martin's Mini Market
Sqft: 7277

Notes:
Concrete surface. Large lot size. Part of cluster of available lots on same
street.

2858 N. Dawson

Overall Score: 6

Distance To Transit: > ½ mile
Nearest Grocer: Adrian's Food Mart
Community Area: Avondale
Sqft: 846

Notes:
Small, odd lot shape. Appears to have present landscaping from
neighboring house.

6145 W. Fullerton

Overall Score: 6

Distance To Transit: .6 mile
Nearest Grocer: Jewel Osco
Community Area: Belmont - Cragin
Sqft: 2696

Notes:
Concrete surface. Across street from Riis Park, Mid-rise residential
nearby.

5911 N. Sheridan Rd.

Overall Score: 6

Distance To Transit: > ¼ mile
Nearest Grocer: Dominick's
Community Area: Edgewater

Notes:
Largest of a few properties in this area. By Loyola University, part of /
close to public parks and beach. Potential for work with university?
Downside: proximity to beach may come with too many scavengers.

2025 W. George

Overall Score: 5

Distance To Transit: 1 mile
Nearest Grocer: Whole Foods, Clybourn Market
Community Area: North Center
Sqft: 2946

Notes:
Fenced-off green space in highly populated area. Within residential area.

1643 N. Clybourn

Overall Score: 6

Distance To Transit: > ¼ mile
Nearest Grocer: Whole Foods, Trader Joe's, Stanley's Fruits and
Vegetables
Nearby Community Gardens? Edgewater Gateway
Community Area: Lincoln Park
Sqft: 2492

Notes:
Possibly too much sunlight blocked by adjacent buildings.

1713 N. Halsted

Overall Score: 6

Distance To Transit: ¼ mile
Nearest Grocer: Trader Joe's, Whole Foods, Stanley's Fruits &
Vegetables
Sqft: 3363

Notes:
Abandoned property on site, need to be removed / renovated /
deconstruction

1439 W. Taylor

Overall Score: 6

Nearest Grocer: Jewel-Osco
Community Area: Near West Side
Sqft: 2663

Notes:
Adequate size greenspace in accessible residential area.

3336 S. Giles

Overall Score: 6

Distance To Transit: ¾ mile
Nearest Grocer: Jewel Osco
Community Area: Douglas
Sqft: 2113

Notes:
IIT / Bronzeville area. Some sunlight blockage by 2-flat adjacent
residential.

312 W. Pershing

Overall Score: 6

Distance To Transit: ¾ mile
Nearest Grocer: Wallace Food & Liquor
Community Area: Douglas
Sqft: 2311

Notes:
By renovated Wentworth Gardens public housing. Just south of sox
stadium. Large nearby plot of land from demolished public building yet
unlisted in city land listings.

1847 N. Sedgwick

Overall Score: 6

Nearest Grocer: Carnival Foods
Nearby Community Gardens? Old Town Triangle Park
Sqft: 9114

Notes:
Interesting existing concrete features. Nearby church with another city
land parcel out front, potential to work with church. May be too much
existing foliage (large trees) to share sunlight.

219 E. 48th

Overall Score: 7

Distance To Transit: ¼ mile
Nearest Grocer: Michael's Fresh Market (>1.5 miles away)
Community Area: Grand Boulevard
Sqft: 8559

Notes:
Very large plot of greenspace in what is clearly a food desert. Accessible
by Green Line 47th st. stop. Nearby 2-3 flat residential.
Many similar cases on South side but too far for majority of current
UHC volunteer base to travel.

Site Selection Work Log Total HRS
205.92
StartTime EndTime Hrs_logged Work / Activity Summary Primary activity / phase
07:00:00 PM 09:00:00 PM 2 Meet – Anna discuss Meetings and Calls
02:00:00 PM 03:30:00 PM 1.5 Meet Cynthia – discuss Meetings and Calls
01:00:00 PM 02:30:00 PM 1.5 Confr Call w/ Cynthia & Q prep Meetings and Calls
11:00:00 AM 06:30:00 PM 7.5 Inf Interview David Baum + Research Green exchange and firms Interviews
11:30:00 AM 08:00:00 PM 8.5 Network Analyst Training, GTFS feed research, other data collection Training, Data Mining
11:00:00 AM 09:00:00 PM 10 Figure out GTFS feed specifications, database setup Data Mining, Data Preparation
11:00:00 AM 08:00:00 PM 9 Access to Pub Transp. Methods research: Variables / formulas, accessibility indexes research Research Analysis Methods
12:30:00 PM 07:00:00 PM 6.5 More attempts to narrow down pubtrans accessibility w/ parameter adjustments Research Analysis Methods
11:30:00 AM 04:30:00 PM 5 Narrowing down acc.transit w/ breakline shortening, begin landuse analysis Research Analysis Methods
06:00:00 PM 07:30:00 PM 1.5 Build Landuse database Data Preparation
01:00:00 PM 05:00:00 PM 4 NonGIS: Research shared space, nonprofit perks, lease types, other comm. Real estate vocab Real Estate Education
06:30:00 PM 09:30:00 PM 3 Started Route Speed method, joins/relates, calculate route speed by database rearrange Research Analysis Methods
09:45:00 PM 11:10:00 PM 1.42 Summarize, Join, relate datasets.. product: map of avg bus speeds Data Preparation
02:30:00 PM 06:00:00 PM 3.5 experiment with alternate / narrow parameters, process Research Analysis Methods
11:30:00 AM 02:30:00 PM 3 data mining – city plats, chicago planning forums Data Mining
03:00:00 PM 04:30:00 PM 1.5 attempt recreate new network w/ bus mph, issues w/ rail mph Data Preparation
04:30:00 PM 06:30:00 PM 2 nongis: Research into more datasets, DOT, NTB, RITA-BTS, Metropulse and Enterprise zones Data Mining
07:00:00 PM 08:30:00 PM 1.5 Prep documents for meeting – maps, work log, sq ft calculations, career services paperwprk Paperwork
09:00:00 AM 12:00:00 PM 3 sqft calculations sketchup, printing documents @ library Paperwork
03:00:00 PM 09:30:00 PM 6.5 Meet w/ Anna, UHC staff meeting Meetings and Calls
12:00:00 PM 05:00:00 PM 5 Meet with Marcos, Leslie, Ariel, Mike R. @ Joy Garden RE SSI proj, volunteer mulch moving @ Joy Garden Meetings and Calls, Research Analysis Methods
12:30:00 PM 04:00:00 PM 3.5 Conference call w/ Cynthia, Research google APIs, GeoJSON spec., community gardening initiatives Meetings and Calls, Data Mining
11:30:00 AM 07:30:00 PM 8 Data mining and comm garden research – google places api, yahoo local api, CDC data Data Mining, Data Preparation
03:00:00 PM 06:30:00 PM 3.5 Yahoo API and Yahoo pipes attempt Data Mining, Data Preparation
07:15:00 PM 09:30:00 PM 2.25 More grocery store data search Data Mining
03:00:00 PM 04:00:00 PM 1 Grocery store data search – TRF, Brookings Institute, PolicyMap Data Mining
01:00:00 PM 06:00:00 PM 5 Assemble / create: Night Owl bus-serviced stops, metra stations, city owned land points Data Preparation
12:30:00 PM 04:00:00 PM 3.5 New NetwAnalyst Service areas processed – create KMLs, contact Cook Co. GIS/IT re: Parcel Data Data Preparation, Analysis
06:30:00 PM 09:30:00 PM 3 Search, dowload OpenStreetMap data, convert xml to shp, etc. Data Mining, Data Preparation
04:00:00 PM 10:00:00 PM 6 Search for grocery store data through UIC and COD resources – begin creating derivative of Standard & Poor's Business data Data Minging, Data Preparation
10:00:00 AM 03:30:00 PM 5.5 prepare for informational interview w/ Lori McCall Vierow, Planning Resources, Inc. and community farm in st. charles. Research garden parameters to consider, research sources of data for new
Paperwork
03:30:00 PM 04:00:00 PM 0.5 Inf int Lori McCall Vierow ASLA Meetings and Calls
08:30:00 AM 04:00:00 PM 7.5 Searchgrocery store data – Dex, Yellow pages, DL and learn data mining sw, assemble & clean data of grocery stores Data Mining, Data Preparation
06:00:00 PM 10:00:00 PM 4 Discover more data – Crime, community gardens, etc. Clean and import to gDb Data Mining, Data Preparation
05:00:00 PM 10:00:00 PM 5 Process Community garden, grocery store, crime density Analysis
11:00:00 AM 03:00:00 PM 4 fix process for crime(s), reprocess, process pop density Analysis
11:00:00 AM 03:30:00 PM 4.5 process pop density attemtps / issues Research Analysis Methods
10:30:00 AM 01:00:00 PM 2.5 reprocess w/ new methods Analysis, Research Analysis Methods
05:00:00 PM 10:45:00 PM 5.75 switch to census block based analysis, model, process Analysis
10:00:00 AM 03:00:00 PM 5 Fix model, reprocess, produce sample work for meeting Analysis, Paperwork
06:30:00 PM 09:00:00 PM 2.5 UHC staff meeting Meetings and Calls
06:30:00 PM 10:15:00 PM 3.75 Browse selected site images, Call w/ Cynthia re deadlines / due dates, Begin table of Contents for portfolio Analysis, Meetings and Calls, Paperwork
10:00:00 AM 05:00:00 PM 7 Portfolio work, attempt to scrape Parcel Photos Paperwork, Data Mining
07:00:00 PM 10:00:00 PM 3 Portfolio work Paperwork
01:00:00 PM 05:00:00 PM 4 Emergency Workaround (site Photos), create dB of photos, join, create file of selected sites Data Mining, Data Preparation
06:00:00 PM 10:30:00 PM 4.5 Get List of selected sites w/ photos, create template for Portfolio maps, begin creating each map Paperwork, Map Production
10:15:00 AM 12:30:00 PM 2.25 Produce Layout for selected site portfolio Paperwork, Map Production
05:00:00 PM 10:30:00 PM 5.5 Produce Sites for portfolio, produce graphs of results, write more Paperwork, Map Production
10:00:00 AM 01:00:00 PM 3 Edit sites, remove and add different site selections Paperwork, Map Production
11:00:00 AM 03:00:00 PM 4 Type up Lori inf. Interview. Produce and insert maps into document Paperwork, Map Production
08:00:00 PM 08:30:00 PM 0.5 Conf.. call w/ Cynthia Meetings and Calls
12:00:00 PM 03:00:00 PM 3 Edit final document, scan Career Services Paperwork Paperwork
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