APPENDIX A: GIS METHODOLOGY

Transcription

1 APPENDIX A: GIS METHODOLOGY

2 This section describes the steps, geoprocessing tools, software, and data sources utilized for the creation of an online mapping tool and quantitative analysis for Public Assets: City-Owned and Leased Properties. This work was conducted by the Municipal Art Society of New York from March to November of The online tool is hosted at Merging COLP with MapPLUTO TM In order to provide information for each individual parcel, MAS merged two NYC data sources: MapPLUTO TM V15.1 and City-Owned and Leased Properties (COLP 2014 v ). Both datasets are available to the public and can be downloaded for free through the Department of City Planning s website: MapPLUTO contains more than seventy fields derived from data maintained by city agencies and is one of the most complete datasets for land use in the city. The COLP dataset compiles information for over 14,000 city-owned parcels. The dataset specifies over 200 individual types of use and identifies properties that are currently leased and those that are committed for sale or long-term leases. MAS utilized ArcMap for the initial part of the merger. The first step in the process was to define the relationship between the MapPLUTO TM shapefile and the COLP table by using the common attribute data field BBL or Borough-Block-Lot number. Once the relationship was established, the file was exported as a shapefile containing attribute data from both datasets. This shapefile was then uploaded into the GIS online platform CARTO. However, since the COLP dataset includes attribute data describing more than one use type per parcel and MapPLUTO TM registers one land use per parcel, the additional land use entries from COLP were not reflected in the merged file. MAS resolved this issue by utilizing SQL capabilities available on the GIS online platform CARTO. SQL stands for Structured Query Language, and according to ANSI (American National Standards Institute), it is the standard language for relational database management systems. APPENDIX A: GIS METHODOLOGY The COLP dataset lists use type entries by cell, and therefore contains duplicate entries for each parcel or BBL number. In order to complete the merger with MapPLUTO TM, we reorganized COLP data from the use type and BBL fields so that additional use type entries could be listed in a single row instead of multiple cells. This also removed duplicate parcel entries from the dataset. The process was done through utilizing CARTO s SQL capabilities. The script used is listed under the CARTO SQL Notes section. The dataset was exported into Microsoft Excel and the use type values were separated into multiple rows. This dataset listed only one BBL per parcel and the corresponding land use values were placed horizontally as use1, use2, use3, etc. This new file was uploaded to CARTO as a CSV table. The final step in the merger was to update the uploaded dataset in CARTO with the new aggregate use type information. This was also done using CARTO s SQL capabilities. The utilized script is listed in this document section titled CARTO SQL notes. Web Maps Infowindow, Display, and Symbology In order to display the additional use type information, we utilized CARTO s infowindow HTML custom capabilities. As users click on properties across the map, the window shows selected attribute data from the layers. The window displays the COLP expanded category mode data field as general land use, the use type data field as specific land use, and includes over a dozen more selected fields in the merged COLP MapPLUTO dataset. An extract of the HTML script is listed in HTML or the Infowindow and the Google Image SQL sections. In order to optimize the streaming of the map and reduce chances of crashing the online platform, we decided not to utilize additional SQL queries to filter and display symbology properties. Instead, we exported separate shapefiles from the merged dataset based on COLP s category code and initially divided the dataset into three categories: (i) properties with a current use (non residential), (ii) residential properties, and (iii) properties with no current uses. PUBLIC ASSETS: CITY-OWNED AND LEASED PROPERTIES 45

3 APPENDIX A: GIS METHODOLOGY In addition, we created a fourth layer, (iv) properties with cultural, recreational, and open space, by extracting properties from the second category (properties with a current use nonresidential). Each of these separate shapefiles contains all the attribute data from the merged COLP and MapPLUTO datasets. As shown in Figure A, we then uploaded the individual shapefiles into CARTO and assigned their corresponding symbology values. Red indicates no use, blue indicates non-residential use, yellow indicates residential use, and green for parks and open space. In addition to the general use categories described above, we also created additional shapefiles for (i) properties that are leased, (ii) properties committed for sale or lease, and (iii) properties based on agency jurisdiction for the four agencies with most parcels. We used the select by attributes tool from ArcMap to select parcels listed in the leased properties and final commitment COLP datafields. For agency jurisdiction, we selected parcels using agencymod, and for state or federal properties we selected parcels using Other Public Authority, State, or Federal Ownership. Both data entries are listed in MapPLUTO TM. Once the parcels were selected, we exported them as additional layers, uploaded them in CARTO (shown in Figure B), and assigned the following symbology values: square hatches for properties that are leased and stripe hatches for properties committed for sale or lease. Symbology for agency jurisdiction would not be altered as it is based on use category. Total List of Utilized Layers Derived from COLP and MapPLUTO Merger Properties with a current use (non residential) Residential properties Properties with no current use Properties with cultural, recreational, and open space uses Properties that are leased Properties that are committed for sale or lease Properties managed by the Department of Citywide Administrative Services (DCAS) Properties managed by the Department of Education (DOE) Properties managed by the Department of Housing Preservation and Development (HPD) Properties managed by the New York City Housing Authority (NYCHA) Properties managed by remaining agencies CARTO SQL Notes Aggregate COLP dataset by BBL Run the following sql: SELECT colp_2014_dcp.bbl, string_agg(colp_2014_dcp.use_type,',') as agg FROM colp_2014_dcp GROUP BY colp_2014_dcp.bbl This consolidates all land-use values for each BBL into one cell. Export the dataset and separate the landuse values in excel. Clean up the dataset to remove duplicates. Finally, upload this new file to CARTO as a CSV (bbl_landuse_all). Update COLP Merged Dataset Add columns to each dataset (12 in this case) and name each use#. Run the following sql for each dataset (split it into two for faster processing): UPDATE FILENAME SET use1 = bbl_landuse_all.use1, use2 = bbl_landuse_all. use2, use3 = bbl_landuse_all.use3, use4 = bbl_landuse_ all.use4, use5 = bbl_landuse_all.use5, use6 = bbl_ landuse_all.use6 FROM bbl_landuse_all WHERE FILENAME = bbl_landuse_all.bbl UPDATE FILENAME SET use7 = bbl_landuse_all.use7, use8 = bbl_landuse_all. use8, use9 = bbl_landuse_all.use9, use10 = bbl_landuse_ all.use10, use11 = bbl_landuse_all.use11, use12 = bbl_ landuse_all.use12 FROM bbl_landuse_all WHERE FILENAME = bbl_landuse_all.bbl 46 PUBLIC ASSETS: CITY-OWNED AND LEASED PROPERTIES

4 APPENDIX A: GIS METHODOLOGY Figure A. Web Map For example, for the file colp_dcp_final_commitment, the sql would look like this: UPDATE colp_dcp_final_commitment.bbl SET use1 = bbl_landuse_all.use1, use2 = bbl_landuse_all. use2, use3 = bbl_landuse_all.use3, use4 = bbl_landuse_ all.use4, use5 = bbl_landuse_all.use5, use6 = bbl_ landuse_all.use6 FROM bbl_landuse_all WHERE colp_dcp_final_commitment.bbl = bbl_ landuse_all.bbl This updates the original dataset to include all the additional land-use columns. (The SQL disappears after the dataset has been updated.) HTML or the Infowindow The language can be altered, but essentially the HTML to display general and specific land use is the following: <p>the use for this property is <b>{{excatcodmo}}</ b>, specifically <b>{{use1}}</b>{{#use2}}, <b>{{use2}}</b>{{/use2}}{{#use3}}, <b>{{use3}}</ b>{{/use3}}{{#use4}}, <b>{{use4}}</b>{{/use4}} {{#use5}}, <b>{{use5}}</b>{{/use5}}{{#use6}}, <b>{{use6}}</b>{{/use6}}.</p> The format for each is: {{#use3}}, <b>{{use3}}</b>{{/ use3}}. The {{#use3}} ---- {{/use3}} pattern allows the text to be optional in the case that there is no value in that cell. Figure B. Web Map Symbology Google Image SQL SELECT *, ' e=350x170&location=' mid_y ',' mid_x '&sensor=false&fov=110' as image FROM colp_dcp_properties_with_no_use_03_28_2016 Quantification and GIS Analysis In order to provide quantifiable indicators, MAS utilized ArcMap capabilities to measure data from the attribute table. The GIS analysis consisted of counting the number of properties and measuring surface areas. For example, in order to count the total number of properties with no use, we opened the shapefile attribute table, displayed all records, and annotated the total number into an Excel spreadsheet (in this case 3,162 parcels). In order to measure the surface area, we opened the attribute table, selected calculate geometry on the shape_area field, chose acres (US), and then annotated the number listed in the sum entry from the summary window (no use properties represent 1,832 acres). This process was repeated for all layers derived from the COLP and MapPLUTO TM merger: Properties with a current use (non residential) Residential properties Properties with no current use Properties with cultural, recreational, and open space uses PUBLIC ASSETS: CITY-OWNED AND LEASED PROPERTIES 47

5 APPENDIX A: GIS METHODOLOGY Properties that are leased Properties that are committed for sale or lease Properties managed by DCAS Properties managed by DOE Properties managed by HPD Properties managed by NYCHA Properties managed by remaining agencies MAS studied the relationship between city-owned parcels with additional layers of information and identified citywide trends. The conducted GIS analysis for this additional group of datasets was similar to the one described above, essentially counting the number of properties and measuring surface areas, but selecting parcels with the select by location tool available in ArcMap. For example, in order to count the total number of residential properties that were located within a New York State remediation site, we conducted the following steps: selected residential properties as the target layer, selected the state remediate sites layer as the source layer, and chose intersect the source layer feature as the spatial selection method for target layer. Once the desired parcels were selected, we opened the shapefile attribute table, displayed selected records, and annotated the total number into an Excel spreadsheet (in this case 140 parcels). In order to measure the surface area, we opened the attribute table, selected calculate geometry on the shape area field, chose acres (US), and then annotated the number listed in the sum entry from the summary window (parcels that fully or partially intersect remediation sites represent 5,255 acres). This process was repeated for each of the following layers: Properties with a current use (non residential) Residential properties Properties with no current use Properties with cultural, recreational, and open space uses We counted the number of properties and measured the area of parcels contained in the COLP MapPLUTO merger by replicating the process of choosing the source layer of the select by location tool for seventeen different layers. These additional layers were grouped into five focus areas: environment, infrastructure, 48 landmarks, population, and rezonings. All datasets are directly or indirectly 1 provided by city, state or federal agencies and are available to the public. All datasets were downloaded in March 2016 and represented the latest publicly available version from the source at the time. Environment 2007 FIRMs 100-Year Floodplain (FEMA): what-is-my-bfe-address-lookup-tool/ 2050 Projected Sea Level Rise 100-year Floodplain (NPCC): Environment/Sea-Level-Rise-Maps-2050s-100- year-floodplain-/hbw8-2bah Designated Freshwater and Tidal Wetlands (NYS DEC): Remediation Sites (NYS DEC): gisdata/ Coastal Zone Boundary (DCP): nyc.gov/site/planning/data-maps/open-data. page#waterfront Infrastructure Sewer Infrastructure (OSA NYC): openseweratlas.tumblr.com/data Subway access (DOITT): cityofnewyork.us/transportation/subway- Entrances/drex-xx56 Landmarks LPC Landmarks and Historic Districts (NYC LPC): Individual-Landmarks/ch5p-r223 Population Median Household Income by Census Tracts (U.S. Census ACS): gov/faces/tableservices/jsf/pages/productview. xhtml?pid=acs_14_5yr_s1901&prodtype=table Number of Residents by Census Tract (U.S. Census ACS): 1 The only datasets that are not directly provided by a government agency are the sewershed and CSO drainage area shapefiles. These GIS files were created by the Open Sewer Atlas NYC project through a combination of outdated NYC DEP shapefiles, publicly available documents like the Waterbody/Watershed Facility Plans, Long Term Control Plans, and Environmetal Impact Statements. They have been updated manually to reflect the most current publicly available pdf documents. These layers are not official and should not be used for planning purposes. They are intended for illustrative and educational purposes only. PUBLIC ASSETS: CITY-OWNED AND LEASED PROPERTIES

6 faces/tableservices/jsf/pages/productview. xhtml?pid=acs_14_5yr_b01003&prodtype=table APPENDIX A: GIS METHODOLOGY Rezonings PLACES Initiative (NYC DCP) : gov/site/planning/plans/places.page Neighborhood Studies (NYC EDC): nycedc.com/project/inwood-nyc-neighborhoodstudy Future Updates Public Assets: City-Owned and Leased Properties is scheduled to be launched during The 2016 Municipal Art Society of New York Summit for New York City. MAS envisions this effort as ongoing, and foresees several iterations in order to improve accuracy and performance. The first update of the project will likely commence in spring 2017, including MapPLUTO TM V17.1 and the latest version of COLP (both datasets are expected to be released by then). Furthermore, MAS will explore partnerships with other organizations to facilitate public interaction and encourage participation in the planning process for these properties. MAS will examine alternatives for integrating an online interface with the maps that would allow for the general public to provide feedback and commentary addressed to individual parcels. PUBLIC ASSETS: CITY-OWNED AND LEASED PROPERTIES 49

7 APPENDIX B: DATA RECOMMENDATIONS

8 During our work on this project, we discovered that there is much to be done to improve current and future city datasets. Inconsistencies, incompatible formats, and other issues outlined below should be addressed to facilitate ease of use. 1. New databases should be made available as a downloadable file in a format that facilitates analysis. Poorly designed websites, disorganized spreadsheets, or un-editable PDF files are common obstacles that limit examination. Databases should be made available to the public in formats that allow evaluation, such as a downloadable CSV file or an Excel spreadsheet. Such files should also include a template that defines abbreviations and explains the fields of information. 2. Moreover, in order to allow correlation and comparison to other datasets, they should incorporate at minimum the following attribute information: identification numbers for the Uniform Land Use Review Procedure (ULURP) and City Environmental Quality Review (CEQR), the agreed timeline for implementation, the estimated allocated budget and funding source, the lead and coordinating City agencies for implementation, status of the project, geographic location based on address and Borough Block Lot (BBL) number, and date of the latest update. 3. MAS also encountered some inconsistencies that made the analysis and merger between the existing MapPLUTO and COLP datasets challenging. The following considerations should be made for updates or any future databases. Organization and structure of information should be consistent across datasets. Specifically, databases should avoid arranging data that creates duplicate entries for each property. The COLP dataset breaks use types into 223 use codes, providing information for a detailed assessment of how these properties are being utilized. An individual property can have more than one use type. For example, Central Park has the following: museum/gallery, theater, garage, reservoir, police station, agency executive office, indoor storage and park. The APPENDIX B: DATA RECOMMENDATIONS COLP database organizes this information vertically (by cells) which requires duplicating each property entry, as result Central Park is duplicated 11 times in the database. In contrast, MapPLUTO does not have replicated, as it lists all pertaining information to a tax lot in a horizontal way (columns). In order to integrate both datasets, MAS staff used Python (a highlevel, general-purpose, interpreted, dynamic programming language) to organize use types from COLP in a horizontal arrangement compatible with MapPLUTO. In order to encourage robust databases and cross examination of City information, consistent organization and structure should be implemented. The attribute fields describing agency jurisdiction varies within the datasets themselves. For example, under MapPLUTO The Metropolitan Transportation Agency (MTA) is listed with dozens of different name variations (METROPOLITAN TRANS/AU, METROPOLITAN TRNSPRTN, MTA- NYCTA, etc). As a result, identifying properties under MTA s jurisdiction is challenging and time consuming. Agency names and other labels should remain consistent within and across different datasets. The attribute field describing ownership under MapPLUTO is often unreliable. The dataset obtains this information from IPIS and provides the following codes: C (City Ownership), M (Mixed City & Private Ownership), O (Other- Public Authority, State or Federal Ownership), P (Private Ownership), X (Fully tax exempt property that could be owned by the city, state, or federal government; a public authority; or a private institution), and blank (unknown, usually private ownership). For example, according to MapPLUTO 12,660 properties are classified as C (City Ownership), but according to COLP there are 14,003 properties. Ownership properties across datasets should be consistent as they are both derived from IPIS. PUBLIC ASSETS: CITY-OWNED AND LEASED PROPERTIES 51