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首页> 外文期刊>Computers，environment and urban systems >Synthesizing spatial interaction data for social science research: Validation and an investigation of spatial mismatch in Wichita, Kansas
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Synthesizing spatial interaction data for social science research: Validation and an investigation of spatial mismatch in Wichita, Kansas

机译：综合空间相互作用数据以进行社会科学研究：堪萨斯州威奇托的验证和空间失配研究

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Rising economic segregation suggests a need to examine constraints to job access by race/ethnicity and economic inequality simultaneously. This often requires detailed socio-spatial interaction data to make progress on theoretical and modeling development, empirical studies and policy insights. Commuting data are commonly used because of its wide availability. Despite excellent work trip datasets from the U.S. Census such as the Census Transportation Planning Package and the Longitudinal Employer-Household Dynamics (LEHD) data, there are often gaps between the data that are available and ideal detailed commuting data suited to models and data analysis. This is because commuting data are available for a limited set of socio-economic dimensions and this coarseness limits researchers in their ability to uncover nuances of place-based generalizations about commuting, either socially or spatially. In one promising approach, an information minimizing technique was proposed as a workable practical method to synthesize disaggregated work trip flows. Because the strength of fit between predicted and observed trips is unknown, this paper validates this method using real commutes disaggregated by income and then synthesizes race-income work trips using LEHD data for the Wichita, Kansas metropolitan statistical area. We find that low-income Whites travel longer distances and have more dispersed travel patterns than all African-American and Asian income groups and that both low- and middle-income groups of all race groups have spatially constrained flows. (C) 2015 Elsevier Ltd. All rights reserved.
机译：日益严重的经济隔离现象表明，有必要同时审查种族/民族和经济不平等对就业机会的限制。这通常需要详细的社会空间互动数据才能在理论和模型开发，实证研究和政策见解方面取得进展。通勤数据由于其广泛的可用性而被普遍使用。尽管美国人口普查提供了出色的工作旅行数据集，例如人口普查运输计划包和纵向雇主家庭动态（LEHD）数据，但可用数据与适合模型和数据分析的理想详细通勤数据之间经常存在差距。这是因为通勤数据可用于有限的一组社会经济维度，并且这种粗糙性限制了研究人员在社会或空间上发现基于通勤的地方概括的细微差别的能力。在一种有前途的方法中，提出了一种信息最小化技术作为一种可行的实用方法，用于综合分解工作行程流。由于预测行程和观察行程之间的拟合强度未知，因此本文使用按收入分类的实际通勤来验证此方法，然后使用堪萨斯州威奇托市统计区域的LEHD数据合成种族收入工作行程。我们发现，与所有非裔美国人和亚洲收入群体相比，低收入白人旅行的距离更长，旅行方式更分散，所有种族的低收入和中等收入群体的流动都受到空间限制。 （C）2015 Elsevier Ltd.保留所有权利。

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