Shade distribution in Pacoima, California
Data files
Nov 12, 2025 version files 621.59 MB
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README.md
2.31 KB
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ShadeMobility.zip
621.59 MB
Abstract
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.
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)
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.