DOI: 10.5061/dryad.pc866t20p
title: Shade distribution in Pacoima, California

description: We used Light Detection and Ranging (LiDAR) data to model urban form,
 mapping shade annual and diurnal cycle and estimate average cumulative
 solar exposure in Pacoima, Los Angeles County, California. 
description: We obtained LiDAR data from the LARIAC (Los Angeles Region
 Imagery Acquisition Consortium captured in early 2020. The LiDAR data has
 a point density between 2–3 points per square meter creating DEM, DSM and
 CHM with 1- meter resolution. We used Cloudcompare software and ArcGIS Pro
 2.9 software to classify and process LiDAR data into Digital Surface
 Models and shadow raster for both trees and built form separately.
 Cloudcompare software was used to filter lidar points to vegetation (high,
 medium, and low) as one layer of lidar points, and filtered buildings as a
 second layer. Both layers were then used with Lastools toolbox on ArcGIS
 Pro to generate digital surface and canopy height models to then be used
 to model shadow using solar parameters of zenith and altitude. The output
 of the tools yielded a raster with extent of shade and unshaded spaces
 identified and the spatial resolution was the same as the digital surface
 models. This raster was reclassified to binary values of shade and solar
 exposed. For each hour we applied new solar zenith and altitude values
 into the calculation. The shade extent and composition varied and expanded
 throughout the afternoon as the sun's angle became more oblique.
 Vegetation shade was assumed to be complete shade with high leaf area
 index assumed. In cases where shade from buildings and tree shade
 overlapped, the higher elevated shade casting feature was given credit as
 source of shade.
description: # Shade distribution in Pacoima, California
 [https://doi.org/10.5061/dryad.pc866t20p](https://doi.org/10.5061/dryad.pc866t20p) ## Summary The dataset accompanying this manuscript includes geospatial and point cloud data used for shade stemming of land surface and vegetation characteristics.  The files are organized by data type and format as follows: .las files:\ These are LiDAR point cloud files containing 3D elevation data (x, y, z coordinates) and intensity values. Each file represents a discrete tile of the study area used to derive surface and canopy models for the analysis.\ \ Example file: L4_6434_1910a.las ### File naming convention L4\_ → LiDAR dataset level or flight line identifier\ 6434\_ → Tile reference number based on map grid coordinates\ 1910a → Row Reference #(1910) and a-d quadrant (a) ### Software Compatibility .las files can be opened and analyzed in: ArcGIS Pro (using LAS dataset tools) QGIS (via the LAS or LAStools plugin) CloudCompare (for 3D visualization) PDAL or LASpy (for Python-based analysis) .tif raster files are compatible with any standard GIS or remote sensing software (e.g., ArcGIS, QGIS, ENVI). .csv files can be opened in spreadsheet software (Excel, Google Sheets) or loaded into Python (pandas) or R. .shp files are compatible with most GIS platforms. ### Context of report These data files provide the spatial foundation for the analyses described in the manuscript, specifically for quantifying topographic and vegetation structure of shade in  the study area.\ \ The raster and tabular datasets summarize processed outputs used in statistical and spatial modeling presented in the results. ## Description of the data and file structure The data are part of a paper to evaluate changes in pedestrian behavior in response to extreme heat events in Pacoima, a neighborhood in Los Angeles, California. All data are contained within the zipped file ShadeMobility.zip Once extracted, the files are as follows. ## Code/software No code or scripts are included in the submission, which can be viewed in any GIS software. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * LiDAR data from the LARIAC (Los Angeles Region Imagery Acquisition Consortium, 2020)