20220210 14003800 1.0 FGDC CSDGM Metadata FALSE DataWarehouse.SDEADMIN.JobCenter_poly_scag 002 2.393 287478.279200 725476.621900 3614777.711100 3862970.862700 1 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_UTM_Zone_11N <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.8'><WKT>PROJCS["NAD_1983_UTM_Zone_11N",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-117.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",26911]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>10000</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>26911</WKID><LatestWKID>26911</LatestWKID></ProjectedCoordinateSystem> 20220207 15331600 20220207 15331600 150000000 5000 FGDC Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.1.14362 Job Centers, Connect SoCal (Polygon) 1 -119.325545 -114.532807 34.909174 32.647637 Job centers are areas with significantly higher 2016 (Connect SoCal base year) employment density than their surroundings. Job centers are identified using a methodology which builds on Kane et al. (2018). A report, web mapping application, and informative video on this research can also be found at https://mfi.soceco.uci.edu/category/quarterly-report/detecting-job-density-over-time/. The rationale for SCAG’s job centers methodology is to identify spaces in the SCAG region whose job density is significantly higher than the areas around them. The purpose is not to identify where most jobs are or where the absolute highest density is; rather, the method identifies localized areas which are denser than the areas surrounding them. The outcome is a more dispersed distribution of job centers than prior methodologies which can serve as local centers of activity and more closely resembling “suburban downtowns.” The traditional methodology, from Giuliano and Small (1991) is to define centers are areas with more than 10,000 jobs and more than 10 jobs per acre. <DIV STYLE="text-align:Left;"><DIV><DIV>Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters.The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. <OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI>Identify local maxima candidates.Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates.</LI><LI>Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. </LI><LI>Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. </LI></OL>A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of <A href="https://youtu.be/ylTWnvCCO54">https://youtu.be/ylTWnvCCO54</A>), with several minor differences which result in a different final map of subcenters: different years and slightly different post-processing steps for InfoUSAdata, video study covers 5-county region (Imperial county not included), and limited to 1km scale subcenters.A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The next step was to qualitatively comparing results at each scale to create the final map of 72 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas. Finally, in order to serve land use and travel demand modeling purposes for Connect SoCal, job centers were joined to their nearest TAZ boundaries. While the identification mechanism described above uses a combination of point and grid cell boundaries, the job centers boundaries expressed in this layer, and used for Connect SoCal purposes, are built from TAZ geographies. In Connect SoCal, job centers are associated with one of three strategies: focused growth, coworking space, or parking/AVR.Data Field/Value description:name: Name of job center based on name of local jurisdiction(s) or other discernable feature.Focused_Gr: Indicates whether job center was used for the 2020 RTP/SCS Focused Growth strategy, 1: center was used, 0: center was not used.Cowork: Indicates whether job center was used for the 2020 RTP/SCS Co-working space strategy, 1: center was used, 0: center was not used.Park_AVR: Indicates whether job center was used for the 2020 RTP/SCS parking and average vehicle ridership (AVR) strategies, 1: center was used, 0: center was not used. nTAZ: number of Transportation Analysis Zones (TAZs) which comprise this center.emp16: Estimated number of workers within job center boundaries based on 2016 InfoUSA point-based business establishment data. Values are rounded to the nearest 1000. acres: Land area within job center boundaries based on grid-based identification mechanism (i.e., not based on TAZ boundaries shown). Values are rounded to the nearest 100. </DIV></DIV></DIV> SCAG Additional references: Giuliano, Genevieve, and K. A. Small. 1991. Subcenters in the Los Angeles region. Regional Science and Urban Economics 21, p. 163-182. Kane, K., Hipp, J. R., & Kim, J. H. 2018. Los Angeles employment centers in the twenty-first century. Urban Studies 55:4, 844-869. See https://cloudfront.escholarship.org/dist/prd/content/qt3d55p4zt/qt3d55p4zt.pdf Job Centers Subcenters Employment Centers SCAG Connect SoCal SB375 <DIV STYLE="text-align:Left;"><DIV><DIV>The job centers layer is an advisory planning tool developed in conjunction with SCAG’s 2020 RTP/SCS, Connect SoCal. This data is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. </DIV></DIV></DIV> external 46ec67fde9ee225dbaffd36eb6e0f9d8 Kevin Kane SCAG Senior Regional Planner Los Angeles CA 90017 kane@scag.ca.gov 900 Wilshire Blvd 17th Floor +1 (213) 236-1828 True Kevin Kane Feature Class 2.393 dataset EPSG 6.13(3.0.1) 72 Simple FALSE 72 TRUE FALSE DataWarehouse.SDEADMIN.JobCenter_poly_scag Feature Class 72 OBJECTID FID OID 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 4 0 0 Feature geometry. Esri Coordinates defining the features. name name String 25 0 0 Name of job center based on name of local jurisdiction(s) or other discernable feature. Focused_Gr Focused_Gr Integer 4 10 0 Indicates whether job center was used for the 2020 RTP/SCS Focused Growth strategy, 0: center was not used. 1: center was used, Cowork Cowork Integer 4 10 0 Indicates whether job center was used for the 2020 RTP/SCS Co-working space strategy, 0: center was not used. 1: center was used, Park_AVR Park_AVR Integer 4 10 0 Indicates whether job center was used for the 2020 RTP/SCS parking and average vehicle ridership (AVR) strategies, 0: center was not used. 1: center was used, nTAZ nTAZ Integer 4 10 0 Number of Transportation Analysis Zones (TAZs) which comprise this center. emp2016 emp2016 Double 8 38 8 Estimated number of workers within job center boundaries based on 2016 InfoUSA point-based business establishment data. Values are rounded to the nearest 1000. acres acres Double 8 38 8 Land area within job center boundaries based on grid-based identification mechanism (i.e., not based on TAZ boundaries shown). Values are rounded to the nearest 100. 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