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 BularzInteriors 2870 – Internship - Transfer

Summer 2011Prof. Cynthia Milota


Table of ContentsIntroduction page

Project Description 3Educational Learning GoalsProject DeliverablesProject Timeline

Methodology 5

Spatial AnalysisData Mining, Data Design 6Network-based Analysis 7Data Manipulation and Queries 8Aggregating all results into final weighted Spatial Analysis 10

Results 11

Trends observedQuality of Results, Methodology Re-examined 12

Result Maps 14

Input Parameters Map 14Analysis Results Map 15Selected Parcels Map 16

Resources (Works Cited in Document) 17Appendix (All works and resources used in project) 18Selected City Parcels 21

A note on selected parcelsSelected Parcels 22Work Log 37


Mike BularzSummer 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,

Manipulation →

Database Creation,

Queries →

Modeling and

Analysis

Results and

ProductsPhase: 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 AnalysisA 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

come out suitable other than where there are rivers

Result: Clear trends visible, suitable pockets

accentuated, better results

Network-based AnalysisDensity, 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

inaccurate results

Result: More accurate, based on travel times

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 QueriesSeveral 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 AnalysisFinally, 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

Distribution

B: Grocery Store

Distribution

C: Population

Distribution

D: Access to Rapid

Transit

(A x 20% + B x 20% + C x 20% + D x 40%) / Max Score = Result Cell Values

→ See Results section for Result Map

ResultsThe 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>.

http://data.cityofchicago.org/

http://www.esri.com/software/arcgis/extensions/spatialanalyst/brochures-


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 portfolioThe 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 MileNearest Grocer: Super Food MartNearby Community Gardens? 3Community Area: Albany ParkSqft: 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 mileNearest Grocer: Martin's mini marketNearby Community Gardens? NoCommunity Area: Portage ParkSqft: 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

Distance To Transit: ½ mileNearest Grocer: Martin's MarketNearby Community Gardens? NoCommunity Area: Portage ParkSqft: 3127

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

3626 N. Cicero

Overall Score: 6

Distance To Transit: ½ mileNearest Grocer: Martin's Mini MarketNearby Community Gardens? NoCommunity Area: Portage ParkSqft: 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 mileNearest Grocer: Adrian's Food MartNearby Community Gardens? NoCommunity Area: AvondaleSqft: 846

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

6145 W. Fullerton

Overall Score: 6

Distance To Transit: .6 mileNearest Grocer: Jewel OscoNearby Community Gardens? NoCommunity Area: Belmont - CraginSqft: 2696

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

5911 N. Sheridan Rd.

Overall Score: 6

Distance To Transit: > ¼ mileNearest Grocer: Dominick'sNearby Community Gardens? NoCommunity 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 mileNearest Grocer: Whole Foods, Clybourn MarketNearby Community Gardens? NoCommunity Area: North CenterSqft: 2946

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

1643 N. Clybourn

Overall Score: 6

Distance To Transit: > ¼ mileNearest Grocer: Whole Foods, Trader Joe's, Stanley's Fruits and VegetablesNearby Community Gardens? Edgewater GatewayCommunity Area: Lincoln ParkSqft: 2492

Notes: Possibly too much sunlight blocked by adjacent buildings.

1713 N. Halsted

Overall Score: 6

Distance To Transit: ¼ mileNearest Grocer: Trader Joe's, Whole Foods, Stanley's Fruits & VegetablesNearby Community Gardens? NoCommunity Area: Lincoln ParkSqft: 3363

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

1439 W. Taylor

Overall Score: 6

Distance To Transit: ½ mileNearest Grocer: Jewel-OscoNearby Community Gardens? NoCommunity Area: Near West SideSqft: 2663

Notes: Adequate size greenspace in accessible residential area.

3336 S. Giles

Overall Score: 6

Distance To Transit: ¾ mileNearest Grocer: Jewel OscoNearby Community Gardens? NoCommunity Area: DouglasSqft: 2113

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

312 W. Pershing

Overall Score: 6

Distance To Transit: ¾ mileNearest Grocer: Wallace Food & LiquorNearby Community Gardens? NoCommunity Area: DouglasSqft: 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

Distance To Transit: ½ mileNearest Grocer: Carnival FoodsNearby Community Gardens? Old Town Triangle ParkCommunity Area: Lincoln ParkSqft: 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 mileNearest Grocer: Michael's Fresh Market (>1.5 miles away)Nearby Community Gardens? NoCommunity Area: Grand BoulevardSqft: 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 HRS205.92

Hrs_logged Work / Activity Summary Primary activity / phase07:00:00 PM 09:00:00 PM 2 Meet – Anna discuss Meetings and Calls02:00:00 PM 03:30:00 PM 1.5 Meet Cynthia – discuss Meetings and Calls01:00:00 PM 02:30:00 PM 1.5 Meetings and Calls11:00:00 AM 06:30:00 PM 7.5 Inf Interview David Baum + Research Green exchange and firms Interviews11:30:00 AM 08:00:00 PM 8.5 Network Analyst Training, GTFS feed research, other data collection Training, Data Mining11:00:00 AM 09:00:00 PM 10 Figure out GTFS feed specifications, database setup Data Mining, Data Preparation11:00:00 AM 08:00:00 PM 9 Research Analysis Methods12:30:00 PM 07:00:00 PM 6.5 Research Analysis Methods11:30:00 AM 04:30:00 PM 5 Research Analysis Methods06:00:00 PM 07:30:00 PM 1.5 Data Preparation01:00:00 PM 05:00:00 PM 4 Real Estate Education06:30:00 PM 09:30:00 PM 3 Started Route Speed method, joins/relates, calculate route speed by database rearrange Research Analysis Methods09:45:00 PM 11:10:00 PM 1.42 Data Preparation02:30:00 PM 06:00:00 PM 3.5 experiment with alternate / narrow parameters, process Research Analysis Methods11:30:00 AM 02:30:00 PM 3 Data Mining03:00:00 PM 04:30:00 PM 1.5 attempt recreate new network w/ bus mph, issues w/ rail mph Data Preparation04:30:00 PM 06:30:00 PM 2 Data Mining07:00:00 PM 08:30:00 PM 1.5 Paperwork09:00:00 AM 12:00:00 PM 3 Paperwork03:00:00 PM 09:30:00 PM 6.5 Meet w/ Anna, UHC staff meeting Meetings and Calls12:00:00 PM 05:00:00 PM 5 Meetings and Calls, Research Analysis Methods12:30:00 PM 04:00:00 PM 3.5 Meetings and Calls, Data Mining11:30:00 AM 07:30:00 PM 8 Data Mining, Data Preparation03:00:00 PM 06:30:00 PM 3.5 Yahoo API and Yahoo pipes attempt Data Mining, Data Preparation07:15:00 PM 09:30:00 PM 2.25 More grocery store data search Data Mining03:00:00 PM 04:00:00 PM 1 Data Mining01:00:00 PM 06:00:00 PM 5 Data Preparation12:30:00 PM 04:00:00 PM 3.5 Data Preparation, Analysis06:30:00 PM 09:30:00 PM 3 Data Mining, Data Preparation04:00:00 PM 10:00:00 PM 610:00:00 AM 03:30:00 PM 5.5 Paperwork03:30:00 PM 04:00:00 PM 0.5 Meetings and Calls08:30:00 AM 04:00:00 PM 7.5 Data Mining, Data Preparation06:00:00 PM 10:00:00 PM 4 Data Mining, Data Preparation05:00:00 PM 10:00:00 PM 5 Process Community garden, grocery store, crime density Analysis11:00:00 AM 03:00:00 PM 4 fix process for crime(s), reprocess, process pop density Analysis11:00:00 AM 03:30:00 PM 4.5 Research Analysis Methods10:30:00 AM 01:00:00 PM 2.5 reprocess w/ new methods Analysis, Research Analysis Methods05:00:00 PM 10:45:00 PM 5.75 switch to census block based analysis, model, process Analysis10:00:00 AM 03:00:00 PM 5 Fix model, reprocess, produce sample work for meeting Analysis, Paperwork06:30:00 PM 09:00:00 PM 2.5 UHC staff meeting Meetings and Calls06:30:00 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, Paperwork10:00:00 AM 05:00:00 PM 7 Portfolio work, attempt to scrape Parcel Photos Paperwork, Data Mining07:00:00 PM 10:00:00 PM 3 Portfolio work Paperwork01:00:00 PM 05:00:00 PM 4 Emergency Workaround (site Photos), create dB of photos, join, create file of selected sites Data Mining, Data Preparation06:00:00 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 Production10:15:00 AM 12:30:00 PM 2.25 Produce Layout for selected site portfolio Paperwork, Map Production05:00:00 PM 10:30:00 PM 5.5 Produce Sites for portfolio, produce graphs of results, write more Paperwork, Map Production10:00:00 AM 01:00:00 PM 3 Edit sites, remove and add different site selections Paperwork, Map Production11:00:00 AM 03:00:00 PM 4 Type up Lori inf. Interview. Produce and insert maps into document Paperwork, Map Production08:00:00 PM 08:30:00 PM 0.5 Meetings and Calls12:00:00 PM 03:00:00 PM 3 Edit final document, scan Career Services Paperwork Paperwork

00

StartTime EndTime

Confr Call w/ Cynthia & Q prep

Access to Pub Transp. Methods research: Variables / formulas, accessibility indexes researchMore attempts to narrow down pubtrans accessibility w/ parameter adjustmentsNarrowing down acc.transit w/ breakline shortening, begin landuse analysisBuild Landuse databaseNonGIS: Research shared space, nonprofit perks, lease types, other comm. Real estate vocab

Summarize, Join, relate datasets.. product: map of avg bus speeds

data mining – city plats, chicago planning forums

nongis: Research into more datasets, DOT, NTB, RITA-BTS, Metropulse and Enterprise zonesPrep documents for meeting – maps, work log, sq ft calculations, career services paperwprksqft calculations sketchup, printing documents @ library

Meet with Marcos, Leslie, Ariel, Mike R. @ Joy Garden RE SSI proj, volunteer mulch moving @ Joy GardenConference call w/ Cynthia, Research google APIs, GeoJSON spec., community gardening initiativesData mining and comm garden research – google places api, yahoo local api, CDC data

Grocery store data search – TRF, Brookings Institute, PolicyMapAssemble / create: Night Owl bus-serviced stops, metra stations, city owned land pointsNew NetwAnalyst Service areas processed – create KMLs, contact Cook Co. GIS/IT re: Parcel DataSearch, dowload OpenStreetMap data, convert xml to shp, etc. Search for grocery store data through UIC and COD resources – begin creating derivative of Standard & Poor's Business data Data Minging, Data Preparationprepare 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 parameters.Inf int Lori McCall Vierow ASLASearchgrocery store data – Dex, Yellow pages, DL and learn data mining sw, assemble & clean data of grocery storesDiscover more data – Crime, community gardens, etc. Clean and import to gDb

process pop density attemtps / issues

Conf.. call w/ Cynthia

Documents

Urban Habitat Chicago - Community Gardening Analysis