Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 11 0;"><SPAN STYLE="font-style:italic;"><SPAN>Data Source: </SPAN></SPAN><SPAN><SPAN>The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by </SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>InfoGroup </SPAN></SPAN><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN STYLE="font-style:italic;"><SPAN>Locally-weighted regression: </SPAN></SPAN><SPAN><SPAN>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</SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>.</SPAN></SPAN><SPAN><SPAN>The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. </SPAN></SPAN></P><OL STYLE="margin:0 0 0 0;padding:0 0 0 0;"><LI><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>Identify local maxima candidates.</SPAN></SPAN><SPAN><SPAN>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 </SPAN></SPAN><SPAN STYLE="font-style:italic;"><SPAN>local maxima </SPAN></SPAN><SPAN><SPAN>based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates.</SPAN></SPAN></P></LI><LI><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>Identify local maxima</SPAN></SPAN><SPAN><SPAN>. 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. </SPAN></SPAN></P></LI><LI><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>Search adjacent cells to include as part of each subcenter</SPAN></SPAN><SPAN><SPAN>. 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. </SPAN></SPAN></P></LI></OL><P STYLE="margin:0 0 11 0;"><SPAN /></P><P STYLE="margin:0 0 11 0;"><SPAN>A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of </SPAN><A href="https://youtu.be/ylTWnvCCO54"><SPAN><SPAN>https://youtu.be/ylTWnvCCO54</SPAN></SPAN></A><SPAN>), with several minor differences which result in a different final map of subcenters: different years and slightly different post-processing steps for </SPAN><SPAN STYLE="font-style:italic;"><SPAN>InfoUSA</SPAN></SPAN><SPAN>data, video study covers 5-county region (Imperial county not included), and limited to 1km scale subcenters.</SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN STYLE="font-weight:bold;">Data Field/Value description:</SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>name: Name of job center based on name of local jurisdiction(s) or other discernable feature.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>nTAZ: number of Transportation Analysis Zones (TAZs) which comprise this center.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>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. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN /><SPAN /></P><P STYLE="margin:0 0 11 0;"><SPAN /><SPAN /></P></DIV></DIV></DIV>
Service Item Id: f20e210ea366429d8b9d0d0745992a4e
Copyright Text: 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
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 0 0;"><SPAN>This layer identifies Neighborhood Mobility Areas (NMA) in the region using the attribute Sum_Zscore as the operative attribute. The Tier 2 TAZs in this layer that have a numerical value of the attribute Sum_Zscore at or above the 80th percentile were NMAs for the purpose of Connect SoCal. The attributes in the file Int_Zscore, L_speed_Zs, Luent_Zsco, and Dist_Sum_Z were summed to create the attribute Sum_Zscore.</SPAN></P><P><SPAN /></P></DIV></DIV></DIV>
Service Item Id: f20e210ea366429d8b9d0d0745992a4e
Copyright Text: Joseph Cryer, Kevin Kane, Ariel Pepper
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 7 0;"><SPAN>This is the High Quality Transit Areas (HQTAs) in the SCAG Region for plan year 2045, developed for the Final Connect SoCal (the 2020-2045 Regional Transportation Plan/Sustainable Communities Strategy (RTP/SCS)). SCAG’s HQTA is within one-half mile from a “major transit stop” and a “high-quality transit corridor” and developed based on the language in SB375 and codified in the CA Public Resources Code. The definitions of a “major transit stop” and a “high-quality transit corridor” are as follows:</SPAN></P><P STYLE="margin:0 0 7 0;"><SPAN><SPAN>A. Major transit stop: A site containing an existing rail or bus rapid transit station, a ferry terminal served by either a bus or rail transit service, or the intersection of two or more major bus routes with a frequency of service interval of 15 minutes or less during the morning and afternoon peak commute periods (CA Public Resource Code Section 21064.3). It also includes major transit stops that are included in the applicable regional transportation. </SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>B. High-quality transit corridor (HQTC): A corridor with fixed route bus service with service intervals no longer than 15 minutes during peak commute hours.</SPAN></SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN><SPAN>Further explanation of the methodology for identifying HQTCs and major transit stops is included in the Connect SoCal Transit Technical Report Appendix.</SPAN></SPAN></P><P STYLE="margin:0 0 7 0;"><SPAN STYLE="font-weight:bold;"><SPAN>PLEASE NOTE </SPAN></SPAN><SPAN><SPAN>that SCAG has made one modification to its methodology of HQTAs development to exclude the one-half mile areas around freeway-running HQTCs where there are no bus stops; the one-half mile areas around bus stops serving those freeway HQTCs remain.</SPAN></SPAN></P><P STYLE="margin:0 0 7 0;"><SPAN>Major transit stops and HQTCs are based on the 2045 plan year transit network of Connect SoCal. </SPAN><SPAN STYLE="font-weight:bold;"><SPAN>PLEASE NOTE </SPAN></SPAN><SPAN><SPAN>that </SPAN></SPAN><SPAN><SPAN>SCAG updates its inventory of planned major transit stops and HQTCs with the adoption of a new RTP/SCS, once every four years. However, transit planning studies may be completed by transit agencies on a more frequent basis than the RTP/SCS is updated by SCAG. Users should consult with the appropriate transit provider(s) to obtain the latest information on planned transit routes, stop locations, and service intervals. 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. </SPAN></SPAN><SPAN>Users should consult with the appropriate transit provider(s) to obtain the latest information on transit routes, stop locations, and service intervals before making determinations regarding CEQA exemption or streamlining. </SPAN></P></DIV></DIV></DIV>
Service Item Id: f20e210ea366429d8b9d0d0745992a4e
Copyright Text: Southern California Association of Governments (SCAG)
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This is a dataset of the Environmental Justice (EJ) areas in the SCAG region. The data was created using the base year 2016 data at the level of SCAG Tier 2 TAZs. EJ Area TAZs were identified if they had a higher concentration of minority population or households in poverty than is seen in the greater SCAG region</SPAN></P><P><SPAN STYLE="font-weight:bold;">Analytical methodology</SPAN></P><P><SPAN>o Calculate the regional share of minority population (minority) and household in poverty (ho_pov1) for each TAZ tier 2</SPAN></P><P><SPAN>o Any TAZ that has a higher percentage than 68.27% minority population OR 15.04% household in poverty will be triggered as EJ area</SPAN></P><P><SPAN>o In Excel, use this formula "=IF(OR(N2>0.6827,O2>0.1504),"EJA","-")"</SPAN></P><P><SPAN>Data year: 2016</SPAN></P><P><SPAN>Data source: SCAG, CalEnviroScreen</SPAN></P></DIV></DIV></DIV>
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This dataset identifies “disadvantaged communities,” so designated by CalEPA for the purpose of SB 535. These have been designated using the CalEnviroScreen 3.0 screening methodology, which helps identify California communities that are disproportionately burdened by multiple sources of pollution. This version updates CalEnviroScreen in a variety of ways, including two new indicators reflecting health and socioeconomic vulnerability to pollution and adjustments in the way some indicators are calculated. More detailed information on CalEnviroScreen 3.0 is available here: http://oehha.ca.gov/calenviroscreen/report/calenviroscreen-30 </SPAN></P></DIV></DIV></DIV>
Description: <DIV STYLE="text-align:Left;"><P STYLE="font-size:16ptmargin:7 0 7 0;"><SPAN><SPAN>This dataset identifies “communities of concern,” and is designated for SCAG’s 2020 Regional Transportation Plan/Sustainable Communities Strategies (RTP/SCS) Environmental Justice Analysis Report. Communities of Concern Data Methodology by Kimberly Clark and Tom Vo, April 26, 2019. Using data from the 2013-2017 ACS 5 Year Estimates at the Census Designated Place level (CDP), calculate total percentage of minority population and total percentage of households in poverty for each CDP. Also using census tract data from the 2013-2017 ACS 5 Year Estimates, tabulate percentage of minority population and total percentage of households in poverty for each City of Los Angeles Community Planning Area (CPAs). Intersect CPAs with Census Tracts and tabulate new totals for partial CPA/Census Tracts based on spatial interpolation. Sum total Poverty, households, minority, and population values for each CPA. Merge CDPs and City of Los Angeles Community Planning Areas to create a single “Place” file for the entire SCAG region. Remove the City of Los Angeles CDP from layer. Tabulate % of households in poverty and % of minority population for each “Place.” Using Rank and Percentile tool in Excel, select the places that are in the upper third in the SCAG region for both % of households in poverty (x > 0. 17096018735) and% minority (x > 0. 78260869565). </SPAN></SPAN></P><DIV><DIV><P STYLE="margin:0 0 11 0;"><SPAN /></P></DIV></DIV></DIV>