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    Urban Habitat Chicago - Community Gardening Analysis Urban Habitat Chicago - Community Gardening Analysis Document Transcript

    • 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
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 2 Table of ContentsIntroduction page Project Description 3 Educational Learning Goals Project Deliverables Project TimelineMethodology 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 10Results 11 Trends observed Quality of Results, Methodology Re-examined 12Result Maps 14 Input Parameters Map 14 Analysis Results Map 15 Selected Parcels Map 16Resources (Works Cited in Document) 17Appendix (All works and resources used in project) 18Selected City Parcels 21 A note on selected parcels Selected Parcels 22 Work Log 37
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 3 Mike Bularz Summer 2011 Urban Habitat Chicago Site Selection Internship Finding Suitable Space for Urban Agriculture Initiatives in ChicagoProject 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 gardensthrough identifying food deserts (areas where the population has low access to produce), andidentifying suitable land for these types of initiatives as well, such as unused city-owned land or othernonprofit or public organizations with suitable land, that could benefit through having fresh food intheir 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 APIs, 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 decisionsProject Deliverables:The project should result in the completion of a portfolio of potential sites, data derivatives related tofood access, and maps of food deserts accessible by public transit.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 4Project Progression / Timeline: Setting a clear timeline of various stages of work to complete the project can be difficultwithout knowing what resources would be available to begin with. Data collection consumed a largeportion of time as various statistics, tables, geographic products exist in many different locations on theweb through different entities. A few web portals were consulted for source data / information forcriteria ranging from real estate listings to obesity rates, and not all were useful in the end due tocompatibility or scale (finding or creating data at a micro-level such as census blocks can be difficult ortime consuming). A sizeable portion of time was spent in aggregating different formats so that theycould be compatible, and eventually line up for comparison and analysis. Also, a portion of time wasspent on online training for specific software modules such as one for network-based analysis, which isexplained further in the methodology section. A general note should be made that the project scope shifted midway throughout the project asthe capabilities (and limitations) of GIS technology were better understood and a more usefulapplication was found. The project intended to find a more permanent location for the non-profit UHCbecame the project to find vacant city land that could be more fruitful as a community garden, whichthe creation and maintaining of is one of UHCs primary activities (Glenn). Another change in the project occurred as more data became available through a revamping ofthe City of Chicago data portal (“Chicagos Data Portal 2.0”). Various new data was released towardsthe end of the project which aided, and somewhat derailed the timeline for the project. Consultations byphone or in person with various people that had knowledge that could be beneficial had some effect onmethodology in the project as well.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 5Methodology 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-basedAnalysis and Weighted Spatial Analysis make up the majority of the methodology for the project. Somedegree of Database Manipulation and Queries was used as well, to a large extent to make datacompatible, and also to create new products. The following illustrates a general timeline of themethodologies employed:Step: Data Mining → Data Cleanup, Database Creation, Modeling and Results and Manipulation → Queries → Analysis ProductsPhase: Data Acquisition and Design Processing for new Information ProductsThe majority of the steps followed a smooth progression but had to be reworked when new data wasdiscovered and was able to be incorporated into the project.Spatial Analysis A common application of GIS technology is Spatial Analysis. Spatial Analysis is theaggregation of multiple criteria that have a spatial (locational component) into a compatible andcomparable format and then the manipulation of this into useful information products. This applicationis often what differentiates simple map products and viewers as trends and phenomena can be put into avisual and defined format that aids the decision making process. Spatial Analysis products save timeand 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 definedboundaries such as census blocks or tracts, respectively) into a grid surface (raster, or collection ofsquare cells) with values representing the criteria or phenomenon. The conversion of input datarepresenting criteria such as population density, distribution of grocery stores, distances from publictransit into a common surface format is how a comparison between all of the input information can bemade, and a resulting product produced. Spatial Analysis served as a big portion of the methodology ofthis project.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 6Fig.1: Raster surface representation of phenomena such as community garden distribution andpopulation distribution A: NeighborSpace Gardens (points) B: Population by Census Block (polygons) → → Result: Density of communtiy gardens surface Result: Population Density surfaceData Mining, Data Design Performing the analysis required for the needs of this project consisted not only of determiningwhat factors to consider, but how to get information representing these factors (data). The process ofgetting necessary information (data mining) creating a suitable data design, which is a design andprocess for aggregating together multiple datasets into a compatible and comparable format(Tomlinson, 93-107). In a geographic information system, data design must take into considerationspatial characteristics of the datasets. For example, data from a USDA study of food deserts wasavailable only at the county level, which served no purpose for analyzing areas within Chicago. Oftentimes it is sought to somehow capture various characteristics / parameters at the most mico-level, orlowest common denominator available. A grid surface with each cell representing a 10 X 10 area was an original design, but whenseen through to analysis, the results seemed to not accurately portray spatial patterns that were beinglooked for (see Figure 2). Some of the combined surfaces in this method received more “points” thanothers per cell and didnt seem to prove anything. This was because the factors, such as populationdensity 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. Eachfactor 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 afterthe initial analysis.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 7Fig.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 pocketscome out suitable other than where there are rivers accentuated, better resultsNetwork-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 relativeconcentrations of these factors well, but when analyzing access to public transportation, which wasseen as a key parameter in selecting a site that is not only suitable but in-line with sustainability – agoal of Urban Habitat Chicago. The primary reason for this is that the movement of people is restrictedby streets and this has to be taken into consideration. Rings depicting buffers of 50, 100, 150 feet arenot suitable – a bus stop might be 50 feet away from a person at a given location if they had the abilityto 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 anetwork of traversible nodes and lines connecting these nodes ("Essential Network AnalystVocabulary"). The lines represent walkable roads in our case, and the nodes turns between roads. Formore 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 thatcan be traveled at 3mph (approximate rate of person walking) was created. The analysis then calculatesdistances from inputs such as bus or train stops, and outputs a result.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 8Fig.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 Chicagowith good access to public transportation. Luckily, data is published by the CTA (Chicago TransitAuthority) in a universal format called the GTFS feed. The GTFS, or General/Google Transit FeedSpecification is a standard for publishing data for public transit agencies so that it can then be pluggeddirectly into a myriad of applications such as route-planning services, schedules, and mobileapplications (“General Transit Feed Specification”). The data from the CTA Developer portalconformed 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 beingthat all bus stop, and CTA trains were used. The bus information had to be taken out of the picture orranked. 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 beranked 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
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 9one of the more intense-reworkings was with the CTA GTFS feed, since a lot of the data had variousrelationships. 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 asfollows: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 ofselections and summaries. One example of information derived was the number of stops per hour foreach route, this was done by summarizing stop times by trip number and routes by number of trips, thisgave a count of how many stops per trip per route. This was divided by 24 hours as the CTA data gavetimes for a given day. A selection was made of stops that only have night-owl service, as this was one
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 10way to classify more active bus routes. Punishing the stops by how much time was lost versus walkingat 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 waslacking, another made up for it, and similar results not identifying any specific clusters in the city werederived. 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. Anoverlay basically performs raster mathematics: each cell in a surface / raster is added up, averaged, orsubtracted. For instance, one may take an elevation surface, and add on to the sea level to show areasaffected 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 4derivatives is to convert each of them into a surface. The next step is to rank each cell in values 0 – 9 toget a set of comparable surfaces. A surface of cells representing distances from grocery stores isincompatible for subtracting from a population surface which contains cells representing how manypeople are estimated for the area. For example: a cell corresponding to x latitude and y longitude has355 people, is 250 feet from the nearest grocery store, and 18 feet from the Red Line. These values areincompatible; each cell must be re-classified on a scale of 0-9 in comparison to all of the other cells ofa given surface. Population score 4 + Grocery score 2 + Transit score 9 = 15 / 27 possible points forthat cell, the cell scores 0.55 / 9. For the purpose of this project, a weighted-overlay is done. This module allows for adding anemphasis on the various factors / combined surfaces. To obtain the final result, 40% importance wasgiven for access to rapid transit, 20% for access to grocery stores, 20% for being far from existingcommunity gardens, and 20% for being in a high population density. Grocery store, community gardendistribution, and population surfaces were created at the census block level, the access to transit surfacewas not, to preserve true distances.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 11Fig5: 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 MapResults The resulting data emphasized some pockets in the city where urban gardening would befeasible, and was extrapolated onto points representing city land parcels. The highest scoring parcelsscored 7 / 9, and there were only a few of these, there was a significant amount of parcels with a scoreof 6, and a majority score 5. A few others received the low score below 5, none scored less than 3. (SeeFigure 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 ofthis project focused on not just identifying food deserts, but ones that are accessible by train, and this iswhy the bias exists. Another trend was that the South side had higher scores because the West side hada 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 andSouth sides. This may be due to higher foreclosure rates, as these areas have the poorer population ofChicago. This may be a cause as to why the two graphs in Figure 6 are very similar.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 12Fig. 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 potentallydifferent approaches. Firstly, other reports have found food deserts in a much more meaningfulmethods. Mari Galaghers 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 healthdata and the food deserts are very high, and prove a poignant, grim point. This project focused on
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 13factors such as public transit access, among other considerations explained further in this report, andthe 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 aneven better result had the constraints of time not been as inhibiting. A midpoint switch of the scope ofthe project from looking for office space, to looking for public land put analysis in about a 1 to 1.5month window. Crime data, which could have been useful to factor in safety concerns for volunteers was notleveraged, even though it was obtained, and processed. The crime data was released for the first timethrough the citys 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 ofhow much crime actually happens in Chicago (“Crimes”). Details of this are too much to digress in thisreport. Grocery store data, which was processed in a manner that simply acquired any food-based retailis populated with records for businesses that arent true sources of nutrition, such as convenience andliquor stores, corner stores, among other things. A retooling of the method of acquiring this data couldcategorize the records (stores) into more useful classes: supermarkets, malls, convenience, etc. If time had permitted, a network of the citys transportation options could be modeled, and thenused to process the resulting high-scoring parcels for true accessibility. Reversing the model to see howmuch of the city could be accessed from each parcel, or better, how much of the population, wouldresult in an even better analysis. The creation of such a dataset and processing all of this informationcould 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.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 14
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 15
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 16
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 17 Resources (Works Cited)Glenn, Anna. Personal Meeting. 22 June 2011."Chicagos Data Portal 2.0."Chicagos 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>.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 18 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
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 19"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
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 20"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 & Poors - Americas." Standard and Poors. Standard and Poors. Web. 29 July 2011. <http://www.standardandpoors.com/home/en/us>. Standard and Poors 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) derivativesExamining 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.
    • Finding Suitable Space for Urban Agriculture Initiatives – Mike Bularz – Summer 2011 21 Selected City ParcelsA note on selected parcels for the portfolio The presented available land is just a glimpse of potential land. A bias was present in selectingparcels closer to the North side of the city, where a majority of Urban Habitat Chicago volunteers notonly live, but are more active in community garden efforts, not only in actual community gardens butties 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 GIStechnology to increase awareness of available land opportunities through not only showing fooddeserts, but making the information publicly available through the construction of a public-map viewerwould be a step in the right direction. My site-selections were biased to UHCs needs and logisticalcapabilities, there might be other organizations that are more active in other endeavors – such asrehabilitating old buildings, deconstruction, etc. that could find these sites more suitable, and find someof 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. Farmers market potential?
    • 3804 N. Cicero Overall Score: 6 Distance To Transit: ½ mile Nearest Grocer: Martins 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 Distance To Transit: ½ mile Nearest Grocer: Martins Market Nearby Community Gardens? No Community Area: Portage Park Sqft: 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 mile Nearest Grocer: Martins Mini Market Nearby Community Gardens? No Community Area: Portage Park 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: Adrians Food Mart Nearby Community Gardens? No 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 Nearby Community Gardens? No 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: Dominicks Nearby Community Gardens? No 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 Nearby Community Gardens? No 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 Joes, Stanleys 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 Joes, Whole Foods, Stanleys Fruits & Vegetables Nearby Community Gardens? No Community Area: Lincoln Park Sqft: 3363 Notes: Abandoned property on site, need to be removed / renovated / deconstruction
    • 1439 W. Taylor Overall Score: 6 Distance To Transit: ½ mile Nearest Grocer: Jewel-Osco Nearby Community Gardens? No 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 Nearby Community Gardens? No 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 Nearby Community Gardens? No 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 Distance To Transit: ½ mile Nearest Grocer: Carnival Foods Nearby Community Gardens? Old Town Triangle Park Community Area: Lincoln 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: Michaels Fresh Market (>1.5 miles away) Nearby Community Gardens? No 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 205.92StartTime 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 & Poors 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 0 0