Pedestrian Demand Index

An estimate of the demand for walking in different areas of the City and County of Denver based on two data variables known to contribute to high levels of walking: population density and employment density. Population density (population per square mile) is sourced from the 2020 Census. Employment density (employment per square mile) is sourced from the Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) Version 7. Population and employment density are added together to generate a combined density value. This combined value is grouped into 10 value groupings using the Quantile method in ArcGIS. The 10 value groupings are then assigned a Pedestrian Demand Index Score (1 – 10), which is displayed by Traffic Analysis Zone. The higher the score, the higher the estimated demand for walking. Note: Due to geographic problems inherent in the 2010 Census block group data, used as the base geographic unit to combine employment and population data, results should be considered estimates.

本数据集基于两项已被证实可显著提升步行出行强度的核心变量——人口密度与就业密度，对丹佛市县（City and County of Denver）各区域的步行出行需求进行估算。其中，人口密度（单位：每平方英里人口数）数据源自2020年美国人口普查；就业密度（单位：每平方英里就业岗位数）数据源自纵向雇主-家庭动态（Longitudinal Employer-Household Dynamics, LEHD）项目的出行起点-终点就业统计（Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics, 简称LODES）第7版。研究人员将人口密度与就业密度相加，得到综合密度值；随后采用ArcGIS（ArcGIS）中的分位数法将该综合密度值划分为10个分组，并为每个分组分配1至10分的步行需求指数得分，最终按交通分析区（Traffic Analysis Zone）展示得分结果。得分越高，对应区域的估算步行出行需求越高。注：由于本研究用于整合人口与就业数据的基础地理单元为2010年人口普查街区组数据，该类数据本身存在固有地理精度缺陷，因此本数据集的结果仅可视为估算值。