{"title":"Access Weight Matrix: A Place and Mobility Infused Spatial Weight Matrix","authors":"Fatemeh Janatabadi, Alireza Ermagun","doi":"10.1111/gean.12395","DOIUrl":null,"url":null,"abstract":"<p>This study introduces the Access Weight Matrix (AWM) to capture the spatial dependence of access across a geographical surface. AWM is a nonsymmetry, nonzero diagonal matrix with elements to be a function of (i) the spatial distribution of places, (ii) the number of places, and (iii) the travel-time threshold to reach places rather than distance, contiguity, or adjacency. AWM is tested and validated to examine the spatial dependence of transit access to employment opportunities in the City of Chicago. Three observations are noticed. First, the degree of spatial dependence between the access of geographical units is not necessarily proportional to their proximity and is better explained by AWM than traditional spatial weight matrices regardless of the travel-time threshold. Second, the time-dependence feature of AWM improves the accuracy of capturing spatial dependence, particularly in short travel-time thresholds. Third, near geographical units are not necessarily more related than distant geographical units even for access that is proved to be spatially highly correlated with neighboring units. With the increased ease of measuring access, research is expanding to explore the socioeconomic, demographic, and built-environment correlates of access. AWM can be employed in developing more accurate spatial econometrics models.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"56 4","pages":"746-767"},"PeriodicalIF":3.3000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12395","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geographical Analysis","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/gean.12395","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
This study introduces the Access Weight Matrix (AWM) to capture the spatial dependence of access across a geographical surface. AWM is a nonsymmetry, nonzero diagonal matrix with elements to be a function of (i) the spatial distribution of places, (ii) the number of places, and (iii) the travel-time threshold to reach places rather than distance, contiguity, or adjacency. AWM is tested and validated to examine the spatial dependence of transit access to employment opportunities in the City of Chicago. Three observations are noticed. First, the degree of spatial dependence between the access of geographical units is not necessarily proportional to their proximity and is better explained by AWM than traditional spatial weight matrices regardless of the travel-time threshold. Second, the time-dependence feature of AWM improves the accuracy of capturing spatial dependence, particularly in short travel-time thresholds. Third, near geographical units are not necessarily more related than distant geographical units even for access that is proved to be spatially highly correlated with neighboring units. With the increased ease of measuring access, research is expanding to explore the socioeconomic, demographic, and built-environment correlates of access. AWM can be employed in developing more accurate spatial econometrics models.
期刊介绍:
First in its specialty area and one of the most frequently cited publications in geography, Geographical Analysis has, since 1969, presented significant advances in geographical theory, model building, and quantitative methods to geographers and scholars in a wide spectrum of related fields. Traditionally, mathematical and nonmathematical articulations of geographical theory, and statements and discussions of the analytic paradigm are published in the journal. Spatial data analyses and spatial econometrics and statistics are strongly represented.