SITE SUITABILITY ANALYSIS
SITE SUITABILITY ANALYSIS - BROOKLYN, NYC COLUMBIA UNIVERSITY GSAPP | ADVANCED SPATIAL ANALYSIS - SPRING 18 LAURA SEMERARO | YASHESH PANCHAL | RAMYA RAMANATHAN Guided by Prof. Leah Meisterlin
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INTRODUCTION
SITE SUITABILITY ANALYSIS
SITE SUITABILITY ANALYSIS Real estate developers have notoriously selected sites for development based on location, but have only recently begun looking at more innovative and data driven methods to find the most appropriate site for development. More innovative methods of site selection could increase returns and allow developers to more easily navigate market conditions by using a more data driven approach.
Catchment (2SFC) Method. Through spatial analysis of commonly looked at attributes for development, such as lot suitability, zoning, population changes and service proximity, areas of the borough with development potential were identified and mapped.
This research provides three methods of multi-criteria selection analysis to compare if different methods yield more useful or accurate results in terms of where development potential is highest. Methods developed for the analysis were defined as the Area Method, the Distance Method and a modified version of the Two Step Floating
How do variations on multi-criteria analysis effect what neighborhoods and lots are displayed as having high development potential and do more intensive methods result in dramatically different results or not?
The main question being asked through this research is :
MAP I: Underdeveloped lots in Brooklyn
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GOALS
SITE SUITABILITY ANALYSIS
Study Design MCDA Literature Review
Identification of Variables
MCDA GIS Research first began in the 1980s and 1990s but journals still saw relatively low numbers of published articles on the topic. It was not until the turn of the century closer to the 2000s that the focus on MCDA and GIS began to pick up, often attributed to wider awareness of the capabilities of GIS, additional programs becoming available for use.
Variables used in the MCDA methodologies and lot analysis include hospitals, schools, flood zone, parks/open space, subway-stops, and the criteria of population density change between 2010 and 2014. Data was found for all variables for years as close to 2014 as possible. This was chosen to compare development currently in 2018 to what ended up being mapped through our methods as having development potential in 2014.
Much of the literature surrounding site-selection that we refer to today focuses on areas with less data than the New York City region. For this reason these methods were adapted to fit the geography of a city for the purposes of this research using the data available for the borough of Brooklyn. The aim of this research is to develop multi-criteria analyses for residential development site selection as well as determine what methods may be most appropriate for developers to use in identifying project sites. The first steps in this process were to identify commonly used criteria in existing literature surrounding site selection. In order to help develop these methods
existing research was referenced to develop methodologies that could be compared, using similar variables, but were distinctly different and would likely yield different results which could be compared.
The literature provides a variety of site-selection goals and methods, including landfill site selection, and waste management sites. Much of the literature has been focused on land value, however their methods also relate closely to development potential in addition to relating to land value approximation.
The following section is an overview of existing literature found, and identification of variables used throughout the three methods developed.
Common variables throughout the literature were schools, hospitals and parks. These are just a few services that are synonymous with residential development and stable property values. In some analyses researchers were avoiding these services to identify suitable locations for waste management, in others researchers were looking for prime services to identify areas prime for residential development. A separate important variable mentioned throughout the literature was transit access as it relates to accessibility. Particularly important in a dense urban setting, higher accessibility is related to a more desirable area for development as it provides existing connectivity and transit opportunity. For this reason the subway stops data from 2015 was included in the analysis as a variable for all three analysis methods.
SITE SUITABILITY ANALYSIS
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SITE SUITABILITY ANALYSIS
The purpose of including population density change was to focus on areas that have seen growth in recent years, indicating the area as an attractive location for new growth. This density change calculation used 5-year ACS data in two parts, the earlier data from ACS of 2007 - 2011, and the latter being 2012- 2016, correlating to estimates for 2010 and 2014 respectively. Developable Lot Layer The developable lot layer was executed separately and used for comparison in all three analyses. In order to create a layer of all lots with development potential in Brooklyn it was first defined what development potential meant. This was defined as lots within a residential zoning district with 50% or less built FAR.With this base standard, it is assumed that any lot with 50% or less developed FAR it would be profitable for a developer to demolish the existing building and redevelop. This would provide two times the floor area resulting in twice as much revenue from the same lot area. In addition to selecting sites that were 50% or less developed, vacant and parking land uses were added to this developable lot layer. This is because a lot in a residential district would not be at its highest and best use as a parking lot or vacant space, indicating development potential among those lots as well. A final step in developable lot selection was to remove lots within the flood zone, as they provide restrictions that would reduce the long term value of the lot.
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SITE SUITABILITY ANALYSIS
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F GG F F G F G G F
F G G F F G F G FG G F F GG F
F G Hospitals
MAP I: Extents of Study Area Open Space
Map I shows the location of the chosen study area for this analysis, Brooklyn New York. Brooklyn in recent years has seen significant development and was chosen as a study area due to the significant changes it has experienced in its neighborhoods over the lat decade.
Underdeveloped Lots
Flood Zones
Schools
Subway Stops
The figure on page 7 shows all the individual variables used throughout the analysis. The variables are mapped separately for reference. Not all methods use every variable, nor do they use them the same way, but efforts were made in development of the methodologies to keep the variables as consistent as possible throughout the methods.
F G G F F G F G FG G F
F GG F F G F G G F
F GG F
Population Density Change: 2010-2014
MAP II: Input Criteria for Analysis 6
F G
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Miles 10
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SITE SUITABILITY ANALYSIS
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AREA METHOD
The area method of analysis uses the census tract as a unit of area for comparison. This method of comparison was a vector based multi criteria analysis. The area method looks at density for the variables of transit, schools, and public facility. These counts were normalized by area of the census tract and each given an index value which contributes to the overall index value. The open-space variable was measured for its access as opposed to simply the presence of a park or not in order to appropriately weight areas with access to parks.This is executed by buffering all parks and weighting the portion of census tract with the normalized value based on the area of the park or park buffer within the census tract. This buffer is a quarter mile distance generally accepted as a walkable radius. Thus the index value for the parks variable is the proportion of the census tract that is covered by a park or park buffer area.
Through the analyses, census tracts with the highest development score would be identified. Vacant lots within those high-value census tracts would then serve as the final step in the process. Once the highest quintile of census tracts is identified, the percentage of developable lot area within the census tract is then joined to the census tract on the map, which of the highest scoring tracts of suitable have available built density for developers to take advantage of. This step is to indicate that even though some census tracts may be more ideal in regards for services, developers will be looking for neighborhoods with the most undeveloped areas where they can invest in. Shown below in the figure below is a schematic diagram of how the Area method is executed in GIS. The census tracts with the highest index values, therefore the highest summation of services per census tract areas, would have the highest index score relating to ability to support residential development.
FIGURE I: Schematic of Method 1 Area Method
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DISTANCE METHOD
SITE SUITABILITY ANALYSIS
SITE SUITABILITY ANALYSIS
The distance method of comparison focuses on how much area each service serves as opposed to strictly the density of the services within a chosen area as seen in Method 1. The area method looks at the density of chosen criterion using distance network analysis rasters of a quarter mile (1320 ft.), a generally accepted walking distance, as opposed to a determined area such as the census tract.The services and the areas they serve, are juxtaposed spatially to reveal neighborhoods that are highly served.
each of the service node were then rasterized for conducting an arithmetic raster summation of unique values assigned to the network polygons in each service layer. Spatial summation of the polygons revealed some high-scoring and low-scoring areas in Brooklyn.
Step 2: Calculating the cumulative proportionate area of high-scoring lots to their underlying MCDA score boundaries to map zones with high development potential. Those MCDA regions which also had a Step 1: Services such as schools, hospitals, subway considerable proportion of developable lots, served stops, and (the edge of ) parks were designated as as the best potential neighborhoods for investment. service nodes. The polygon features derived through a network analysis of walkable distances around
FIGURE II: Schematic of Method 2 Distance Method
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TWO STEP FLOATING CATCHMENT METHOD
SITE SUITABILITY ANALYSIS
SITE SUITABILITY ANALYSIS The Two Step Floating Catchment Method in this study is a modified version of the traditional two step floating catchment method used in typical spatial analysis.This was modified to work with our constraints and our variables as well as to better work as a comparison for the initial two methods. The intent of the two step floating catchment is to determine demand on the individual service nodes as well as the coverage of those service nodes, thereby measuring the appropriateness of the area in two directions. Step 1: Services used in this analysis included subways, hospitals, and schools. Parks were initially included in this analysis but were removed due to the amount which they skewed the results. The parks data did not provide a simplified shapefile that would allow for a more streamlined analysis, therefore the extensive number of nodes inaccurately inflated the service index value in certain areas.
In step one the above mentioned services were given network analysis buffers of a quarter mile (1320 ft) similar to Method 2. With these network buffers, population numbers from the 2017 ACS five year census data were aggregated to these network areas and attributed to each individual node. This analysis provided each node with a “Service Demand� number, as the value of each node was given by 1 over the population. Therefore the inverse of the population would provide a higher index value for services that served fewer people, indicating potential for the node to support additional demand. Step 2: Step two is a step similar to Method 1. Once each service node was given a demand value, the nodes were summed based on location within a census tract, used as a unit of area for this analysis.The intent of the methodology design was to provide a measure of the areas potential to service additional residents.
FIGURE III: Schematic of Method 3 Modified Two Step Floating Catchment
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3 METHOD
COMPARISON
RESULTS
After all three methods were executed their results were analyzed both individually on basis of their individual methodology as well as in comparison to each other to determine what each was measuring well and if there were results that were unexpected. The analysis were labeled 1, 2, and 3 in order of increasing complexity, the area method being the simplest, the distance being slightly more complex, and the two step floating catchment method being the most complex and taking into account the most factors with regards to the services ability to cover an area.
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The following pages show both the multicriteria decision analysis results as well as the overlay of development potential for methods 1 and 2. Major takeaways from these methods are that more complicated method does not necessarily yield more accurate results and that as a researcher you must be aware of what you’re measuring and what your results are telling you, even if they are not as expected. For easy reference all high scoring areas are outlined in red for ease of visibility and comparison of high scoring areas.
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AREA METHOD
SITE SUITABILITY ANALYSIS
Map III shows the results of the Area Method MCDA methodology. Index values from the summation of service nodes ranged from 0 to 3.00, with the top quintile ranging from 1.80 to 3.00. These top scoring census tracts were widespread throughout the borough but there were patterns concentrated in certain areas. This overall analysis indicated the following neighborhoods had the highest potential for supporting residential development:
MAP III.I: Area Method Mapped by Development Potential
- Downtown Brooklyn - Crown Heights - Bushwick - Gowanus - Fort Greene - Bed Stuy - Williamsburg - Borough Park
As mentioned the intent was to find areas that supported residential development but also had development potential. Selecting out these top scoring census tracts, our developable lot layer was then spatially joined and normalized by census tract area to provide a proportion of developable area within the census tract space to highlight which of the high scoring areas could support additional development. The results of this second step can be seen in the overall Map III.I and inset map III.II. These results further highlighted specific areas which were located in the neighborhoods listed below: - Crown Heights - Bushwick - Williamsburg - Bed Stuy
MAP III.II: Close up of Highest Scoring Areas
The area method has limitations in that it does not determine the capacity of services to serve the surrounding area. Overall as a measurement of characteristics in a the surrounding area, it does execute the measurement as anticipated. MAP III: Method I MCDA Analysis Results for Area Method
SITE SUITABILITY ANALYSIS
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DISTANCE METHOD
SITE SUITABILITY ANALYSIS
Map IV shows results of the MCDA analysis after superimposing the network distance polygons for the service area for each of the service nodes. The overlap between the service areas of each of the nodes demarcates the level of service received by each area of Brooklyn. The analysis, like the others, does not differentiate between types of services but all are weighted equally. The analysis was raster based, therefore the cell values added resulted in scores that highlighted several high value zones, those served the most services, namely hospitals, schools, subway stops and parks. The scores vary from the top-scoring ‘4’ through ‘0’, 4 being the highest possible score and 0 being an area not served at all by the chosen variables.
MAP IV.I: Highest Scoring Areas Mapped by Development Potential
Some of the ares that had the highest indication of services is shown outlined in red in Map IV and then mapped in Maps IV.I and IV.II with respect to their individual development potential. The top quartile of areas from the index score were similar to the neighborhoods specified in the Area method, but more targeted. The neighborhoods identified through Map IV are listed below:
- Park Slope - Bushwick - Borough Park - Bed Stuy
MAP IV.II: Close up of High Scoring Areas
The neighborhoods listed above also had a high proportion of underdeveloped lots. The distance-method could serve as a useful tool for developers to locate potential investments in high-value areas. The method however has certain limitations. Since it uses network distances, it cannot account for the population density, an important factor in determining the value of a said neighborhood. Also, proximity to a service may not necessarily mean that the service has additional capacity to serve the potential influx. This measure could be better understood through an analysis using the 2SFC method. MAP IV: Distance Method Results
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2SFC METHOD
SITE SUITABILITY ANALYSIS
The Two Step Floating Catchment Method yielded the most drastically different results from the first two analysis methods, seen mapped in Map V. As seen below only two census tracts were shown to be hot spots for potential development, neither of which are known to be high residential areas. Additionally some of the second tier scoring census tracts are also known to have limited residential area.
MAP V.I: Overall map of two step method with locator
DUMBO and East Flatbush are the two neighborhoods encompassing the highest scoring census tracts shown. DUMBO specifically is known for having more office and industrial type spaces as opposed to residential development.The same can be said for both Red Hook and Gowanus. Gowanus at the moment is going through a rezoning, as it currently does not have residential zoning in the area. Upon close inspection of the map and review of the developable lots process, it is clear that there are no developable residential lots in Gowanus, despite its plethora of underutilized lots due to its current zoning which is mostly manufacturing. One interpretation of these results from this analysis is that this tool while it may not predict specific residential areas, does seem to predict areas that lack residents but have services. This method therefore could be used as a tool to identify areas already primed for additional residential development, utilized by planners as opposed to developers.
MAP V.II: Close-up of high scoring areas with development potential lots overlaid
MAP V:Two Step Floating Catchment Method Results
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Planning Takeaways and Analysis comparisons The three methods chosen for analysis in this report provided two somewhat similar results and one very different result, all measuring in different ways development potential in Brooklyn Neighborhoods. While each method intended to measure the same thing, these results are an example of the variety of ways the same data can be manipulated to answer a particular question.
SUMMARY
One major takeaway from the analysis is that methods 1 and 2 show neighborhood development potential similar to areas targeted by developers in recent years. Neighborhoods such as Borough Park, Williamsburg, Bushwick and Bed Stuy have all seen a flood of new development in recent years.This would seem to indicate both of these methodologies are good indicators of where development could occur and where developers should focus as it relates to development potential and underdeveloped areas.
SITE SUITABILITY ANALYSIS
Method 3 while not producing results we expected and the results not answering the specific question we were looking at, the two step floating catchment still provided unique insight
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into overall development potential in Brooklyn. Due to the inverse measurement of services and density analysis, the results of Method 3 seem to indicate better what areas could be further developed or rezoned to sustain residential development. The highest scoring areas lacked underdeveloped lots only because it was residential zoning that was looked at as opposed to other zoning types. Gowanus is an example that is currently being rezoned to further sustain residential development, something our analysis points out. This method could be more useful to planners when considering growth of the city rather than to developers. Overall the scores indicate that a more complicated method may not yield more accurate or detailed results. As seen in Method 1, the neighborhoods identified agree with current development trends just as well as the more complicated Method 2 which incorporated networks, and both which are more indicative of development trends than Method 3. When looking into analysis of areas planners should not be mistaken in thinking that more complex yields a more accurate analysis.
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BIBLIOGRAPHY
SITE SUITABILITY ANALYSIS
Samad, Rokshana Binta and Khan Mahbub Morshed. 2016. “GIS Based Analysis for Developing Residential Land Suitability.” Journal of Settlements and Spatial Planning 7 (1): 23-34. doi:http://dx.doi.org.ezproxy.cul.columbia. edu/10.19188/03JSSP012016. < h t t p : / / e z p r o x y. c u l . c o l u m b i a . e d u / l o g i n ? u r l = h t t p s : / / s e a r c h - p r o q u e s t - c o m . e z p r o x y. c u l . c o l u m b i a . e d u / docview/1807743943?accountid=10226> Abramovich, Adriana Alicia. 2012. Using GIS to Assist Location and Site Selection Decisions. < h t t p : / / e z p r o x y. c u l . c o l u m b i a . e d u / l o g i n ? u r l = h t t p s : / / s e a r c h - p r o q u e s t - c o m . e z p r o x y. c u l . c o l u m b i a . e d u / docview/1372053972?accountid=10226> Li, Heng, Ling Yu, and Eddie W. L. Cheng. 2005. “A GIS-Based Site Selection System for Real Estate Projects.” Construction Innovation 5 (4): 231-241. doi:http://dx.doi.org.ezproxy.cul.columbia.edu/10.1191/1471417505ci101oa. < h t t p : / / e z p r o x y. c u l . c o l u m b i a . e d u / l o g i n ? u r l = h t t p s : / / s e a r c h - p r o q u e s t - c o m . e z p r o x y. c u l . c o l u m b i a . e d u / docview/218324477?accountid=10226>
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DATA SOURCES NYC Department of Education, School Point Locations, 2017 NYC DCP, MapPluto_14v2, 2014 NYC DCP, MapPluto_17V1.1, 2017 US Census, B01003 ACS 2012 & ACS 2017 5-year NYU Spatial Data Repository 2015 New York City Hospitals, 2015 Baruch / CUNY stops_nyc_subway_may2015, 2015 NYC DCP, Census Blocks 2010, 2010 FEMA, NYC Flood Zone data
Sorrentino, John A., Md Mahbubur R. Meenar, and Bradley J. Flamm. 2008. “Suitable Housing Placement: A GIS-Based Approach.” Environmental Management 42 (5): 803-820. < h t t p : / / e z p r o x y. c u l . c o l u m b i a . e d u / l o g i n ? u r l = h t t p s : / / s e a r c h - p r o q u e s t - c o m . e z p r o x y. c u l . c o l u m b i a . e d u / docview/21290875?accountid=10226> Yalpir, Sukran S. Savas Durduran, Fatma Bunyan Unel and Melisa Yolcu. 2014. “Creating a Valuation Map in GIS through Artificial Neural Network Methodology: A Case Study” Acta Montanistica Slovaca 18 (2): 1335 - 1788. Zeng, Thomas Q. and Quiming Zhou. 2001. “Optimal Spatial decision making using GIS: a prototype of a real estate geographical information system (REGIS).” Geographical Science 15 (4) 307 - 321. Malczewski, Jacek. 2006. “GIS-based multicriteria decision analysis: a survey of the literature.” International Journal of Geographical Information Science. 20 (7) 703-726. Wyatt, Peter J. 1997. “The development of a GIS-based property information system for real estate valuation.” International Journal of Geographical Information Science. 11 (5) 435 - 450 Ghita, Cornel. 2014. “A Decision Support System for Business Location Based on Open GIS Technology and Data.” Managing Global Transitions. 12 (2) 101-120 Ding, C., Simons, R., & Baku, E. (2000). The effect of residential investment on nearby property values: Evidence from cleveland, ohio. The Journal of Real Estate Research, 19(1), 23-48. Retrieved from <http://ezproxy.cul.columbia.edu/ login?url=https://search.proquest.com/docview/200331259?accountid=10226> Chang, Ni-Bin, Parvathinathan, G., Breeden, Jeff B. (2008). Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region. Journal of Environmental Management. 87(1), 139-153. Retrieved from <http://www.sciencedirect.com/science/article/pii/S0301479707000230> Cheng Cheng, Thompson, Russell G. (2016). Application of boolean logic and GIS for determining suitable locations for Temporary Disaster Waste Management Sites. International Journal of Disaster Risk Reduction. 20, 78-92. Retrieved from <http://www.sciencedirect.com/science/article/pii/S2212420916301881> Keenan, Jesse M., Wilson, Luc, Hsieh, Mondrian. (2015). Exploratory Impact Analysis of the New York City Zoning Amendment for Quality and Affordability.
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COLUMBIA UNIVERSITY GSAPP | ADVANCED SPATIAL ANALYSIS - SPRING 18 LAURA SEMERARO | YASHESH PANCHAL | RAMYA RAMANATHAN Guided by Prof. Leah Meisterlin
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