{"title":"Correction to “A hybrid approach for mass valuation of residential properties through geographic information systems and machine learning integration”","authors":"","doi":"10.1111/gean.12409","DOIUrl":"https://doi.org/10.1111/gean.12409","url":null,"abstract":"","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Statistical research on correlation with spatial data dates at least to Student's (W. S. Gosset's) 1914 paper on “the elimination of spurious correlation due to position in time and space.” Since 1968, much of this work has been organized around the concept of spatial autocorrelation (SA). A growing statistical literature is now organized around the concept of “spatial confounding” (SC) but is estranged from, and often at odds with, the SA literature and its history. The SC literature is producing new, sometimes flawed, statistical techniques such as Restricted Spatial Regression (RSR). This article brings the SC literature into conversation with the SA literature and provides a theoretically grounded review of the history of research on correlation with spatial data, explaining some of its implications for the the SC literature. The article builds upon principles of plausible inference to synthesize a guiding theoretical thread that runs throughout the SA literature. This leads to a concise theoretical critique of RSR and a clarification of the logic behind standard spatial‐statistical models.
关于空间数据相关性的统计研究至少可以追溯到学生（W. S. Gosset）于 1914 年发表的关于 "消除由于时间和空间位置造成的虚假相关性 "的论文。自 1968 年以来，这方面的大部分工作都是围绕空间自相关（SA）的概念展开的。现在，越来越多的统计文献围绕 "空间混杂"（SC）的概念展开，但这些文献与 SA 文献及其历史相去甚远，而且经常发生冲突。空间混杂 "文献正在产生新的，有时是有缺陷的统计技术，如受限空间回归（RSR）。本文将 SC 文献与 SA 文献结合起来，从理论上回顾了空间数据相关性研究的历史，并解释了其对 SC 文献的一些影响。文章以似是而非的推论原则为基础，综合了贯穿整个空间数据文献的指导性理论主线。这导致了对 RSR 的简明理论批评，并澄清了标准空间统计模型背后的逻辑。
{"title":"Plausible Reasoning and Spatial‐Statistical Theory: A Critique of Recent Writings on “Spatial Confounding”","authors":"Connor Donegan","doi":"10.1111/gean.12408","DOIUrl":"https://doi.org/10.1111/gean.12408","url":null,"abstract":"Statistical research on correlation with spatial data dates at least to Student's (W. S. Gosset's) 1914 paper on “the elimination of spurious correlation due to position in time and space.” Since 1968, much of this work has been organized around the concept of spatial autocorrelation (SA). A growing statistical literature is now organized around the concept of “spatial confounding” (SC) but is estranged from, and often at odds with, the SA literature and its history. The SC literature is producing new, sometimes flawed, statistical techniques such as Restricted Spatial Regression (RSR). This article brings the SC literature into conversation with the SA literature and provides a theoretically grounded review of the history of research on correlation with spatial data, explaining some of its implications for the the SC literature. The article builds upon principles of plausible inference to synthesize a guiding theoretical thread that runs throughout the SA literature. This leads to a concise theoretical critique of RSR and a clarification of the logic behind standard spatial‐statistical models.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To minimize the disclosure of personal information, sensitive location data collected by mobile phones is often aggregated to predefined geographic units and presented as counts of devices at a given time. The use of grids or units created by statistical agencies for the dissemination of traditional data sets—such as censuses—are common choices for this aggregation process. However, these can result in large variations in the number of devices encapsulated within each geographic unit, resulting in over‐generalization and a loss of information in some areas. To alleviate this issue, we propose a new method for the aggregation of mobile phone generated location data sets that creates bespoke geometries that maximize the granularity of the data, whilst minimizing the risks of disclosing personal information. The resulting small areas are built on Uber's H3 hexagonal indexing system by attributing activity counts and land‐use features to each cell, then merging cells into geographies containing a predetermined number of data points and respecting the underlying topography and land use. This methodology has applications to widely available data sets and enables bespoke geographical units to be created for different contexts. We compare the generated units to established aggregates from the England and Wales Census and Ordnance Survey. We demonstrate that our outputs are more representative of the original mobile phone data set and minimize data omission caused by low counts. This speaks to the need for a data‐driven and context‐driven regionalization methodology.
{"title":"The Regionalization and Aggregation of In‐App Location Data to Maximize Information and Minimize Data Disclosure","authors":"Louise Sieg, James Cheshire","doi":"10.1111/gean.12406","DOIUrl":"https://doi.org/10.1111/gean.12406","url":null,"abstract":"To minimize the disclosure of personal information, sensitive location data collected by mobile phones is often aggregated to predefined geographic units and presented as counts of devices at a given time. The use of grids or units created by statistical agencies for the dissemination of traditional data sets—such as censuses—are common choices for this aggregation process. However, these can result in large variations in the number of devices encapsulated within each geographic unit, resulting in over‐generalization and a loss of information in some areas. To alleviate this issue, we propose a new method for the aggregation of mobile phone generated location data sets that creates bespoke geometries that maximize the granularity of the data, whilst minimizing the risks of disclosing personal information. The resulting small areas are built on Uber's H3 hexagonal indexing system by attributing activity counts and land‐use features to each cell, then merging cells into geographies containing a predetermined number of data points and respecting the underlying topography and land use. This methodology has applications to widely available data sets and enables bespoke geographical units to be created for different contexts. We compare the generated units to established aggregates from the England and Wales Census and Ordnance Survey. We demonstrate that our outputs are more representative of the original mobile phone data set and minimize data omission caused by low counts. This speaks to the need for a data‐driven and context‐driven regionalization methodology.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geographic shape has long been an intriguing feature of observed and defined facets of an area or region. Compactness reflects a critical element of shape with important practical and policy implications. It may suggest characteristics of urban/regional form, efficiency in trade and service provision, fairness in political representation and distributional qualities of the physical environment, among others. While there has been much study of compactness and a wealth of measures and metrics derived to reflect nuances of geographic form, there are questions that remain about their ability to characterize shape in a meaningful manner. Given this, exploration of relationships between various categories of methods for quantifying compactness is critical. Further, recent developments of, advances in and access to physics based spatial measures of compactness suggest an opportunity for better theoretical understanding. Assessment of 388 districts is carried out. Significant correlation is demonstrated between contemporary measures, opening the door for research advancements associated with the compactness of spatial shapes. This work is interesting, important, and of current relevance because compactness measures are given serious consideration in management, planning, and policy, but also are regularly relied upon in legal proceedings. Further, compactness measures continue to drive automated and semi‐automated approaches in districting and redistricting, often embedded in optimization approaches.
{"title":"Geographical Compactness in Shape Assessment","authors":"Alan T. Murray","doi":"10.1111/gean.12407","DOIUrl":"https://doi.org/10.1111/gean.12407","url":null,"abstract":"Geographic shape has long been an intriguing feature of observed and defined facets of an area or region. Compactness reflects a critical element of shape with important practical and policy implications. It may suggest characteristics of urban/regional form, efficiency in trade and service provision, fairness in political representation and distributional qualities of the physical environment, among others. While there has been much study of compactness and a wealth of measures and metrics derived to reflect nuances of geographic form, there are questions that remain about their ability to characterize shape in a meaningful manner. Given this, exploration of relationships between various categories of methods for quantifying compactness is critical. Further, recent developments of, advances in and access to physics based spatial measures of compactness suggest an opportunity for better theoretical understanding. Assessment of 388 districts is carried out. Significant correlation is demonstrated between contemporary measures, opening the door for research advancements associated with the compactness of spatial shapes. This work is interesting, important, and of current relevance because compactness measures are given serious consideration in management, planning, and policy, but also are regularly relied upon in legal proceedings. Further, compactness measures continue to drive automated and semi‐automated approaches in districting and redistricting, often embedded in optimization approaches.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data on neighborhood characteristics are not typically collected in epidemiological studies. They are however useful, for example, in the study of small‐area health inequalities and may be available in social surveys. We propose to use kriging based on semi‐variogram models to predict values at nonobserved locations with the aim of obtaining indicators of neighborhood characteristics of epidemiological study participants. The spatial data available for kriging is usually sparse at small distance and therefore we perform a simulation study to assess the feasibility and usability of the method as well as a case study using data from the RECORD study. Apart from having enough observed data at small distances to the non‐observed locations, a good fitting semi‐variogram, a larger range and the absence of nugget effects for the semi‐variogram models are factors leading to a higher reliability. Recommendations on the required number of observations within the neighborhood range are given.
流行病学研究通常不会收集邻里特征数据。不过，这些数据在研究小区域健康不平等现象等方面很有用，而且在社会调查中也可以获得。我们建议使用基于半变量图模型的克里金法预测非观察地点的数值，目的是获得流行病学研究参与者的邻里特征指标。可用于克里金法的空间数据通常在小范围内比较稀少，因此我们进行了一项模拟研究，以评估该方法的可行性和可用性，并利用 RECORD 研究的数据进行了一项案例研究。除了在与非观测点距离较小的地方有足够的观测数据外，拟合良好的半变量图、较大的范围以及半变量图模型不存在金块效应都是导致较高可靠性的因素。本文就邻域范围内所需的观测数据数量提出了建议。
{"title":"Feasibility of Using Survey Data and Semi‐variogram Kriging to Obtain Bespoke Indices of Neighborhood Characteristics: A Simulation and a Case Study","authors":"Emily Finne, Odile Sauzet","doi":"10.1111/gean.12401","DOIUrl":"https://doi.org/10.1111/gean.12401","url":null,"abstract":"<jats:italic>Data on neighborhood characteristics are not typically collected in epidemiological studies. They are however useful, for example, in the study of small‐area health inequalities and may be available in social surveys. We propose to use kriging based on semi‐variogram models to predict values at nonobserved locations with the aim of obtaining indicators of neighborhood characteristics of epidemiological study participants. The spatial data available for kriging is usually sparse at small distance and therefore we perform a simulation study to assess the feasibility and usability of the method as well as a case study using data from the RECORD study. Apart from having enough observed data at small distances to the non‐observed locations, a good fitting semi‐variogram, a larger range and the absence of nugget effects for the semi‐variogram models are factors leading to a higher reliability. Recommendations on the required number of observations within the neighborhood range are given.</jats:italic>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large cellular phone‐based mobility datasets are an important new data source for research on human movement. We investigate and illustrate bias in representation in a large mobility data set at the census block group, tract, and county levels. We paired American Community Survey (ACS) 2019 data with SafeGraph (SG) cell phone mobility data to elucidate potential bias in SG data by examining ACS estimated population against the number of devices in the SG data, stratifying by key sociodemographic variables such as income, percent Black population, percent of population over 55 years, percent of population 18–65 years, percent of people living in crowded living conditions, and urbanization level. We evaluated whether the bias varied over time by examining a 10‐month period. This bias changes with key demographic characteristics and changes over time. Specifically, we see underrepresentation in areas that have the highest percentage of Black population at all aggregation levels. We also see underrepresentation at all levels in areas with the highest percentage of working age residents as well as areas with the lowest median incomes. Researchers should be cautious when using mobility datasets because of bias differential on key sociodemographic factors and collection time.
{"title":"Uncovering Representation Bias in Large‐scale Cellular Phone‐based Data: A Case Study in North Carolina","authors":"Hanna V. Jardel, Paul L. Delamater","doi":"10.1111/gean.12399","DOIUrl":"https://doi.org/10.1111/gean.12399","url":null,"abstract":"Large cellular phone‐based mobility datasets are an important new data source for research on human movement. We investigate and illustrate bias in representation in a large mobility data set at the census block group, tract, and county levels. We paired American Community Survey (ACS) 2019 data with SafeGraph (SG) cell phone mobility data to elucidate potential bias in SG data by examining ACS estimated population against the number of devices in the SG data, stratifying by key sociodemographic variables such as income, percent Black population, percent of population over 55 years, percent of population 18–65 years, percent of people living in crowded living conditions, and urbanization level. We evaluated whether the bias varied over time by examining a 10‐month period. This bias changes with key demographic characteristics and changes over time. Specifically, we see underrepresentation in areas that have the highest percentage of Black population at all aggregation levels. We also see underrepresentation at all levels in areas with the highest percentage of working age residents as well as areas with the lowest median incomes. Researchers should be cautious when using mobility datasets because of bias differential on key sociodemographic factors and collection time.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ane Rahbek Vierø, Anastassia Vybornova, Michael Szell
Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, better bicycle infrastructure networks are required. Planning such networks requires high‐quality infrastructure data, yet the quality of bicycle infrastructure data is understudied. Here, we compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network‐based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high‐quality bicycle network data.
{"title":"How Good Is Open Bicycle Network Data? A Countrywide Case Study of Denmark","authors":"Ane Rahbek Vierø, Anastassia Vybornova, Michael Szell","doi":"10.1111/gean.12400","DOIUrl":"https://doi.org/10.1111/gean.12400","url":null,"abstract":"Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, better bicycle infrastructure networks are required. Planning such networks requires high‐quality infrastructure data, yet the quality of bicycle infrastructure data is understudied. Here, we compare the two largest open data sets on dedicated bicycle infrastructure in Denmark, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether the data are good enough for network‐based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data conflation is necessary to obtain a more complete data set. Our analysis of the spatial variation of data quality suggests that rural areas are more prone to incomplete data. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features is thus necessary to assess data completeness. Based on our data quality assessment, we recommend strategic mapping efforts toward data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high‐quality bicycle network data.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ouidad Benhlima, Fouad Riane, Jakob Puchinger, Hicham Bahi
Rapid urbanization and expansion, stemming from demographic growth and migration from rural areas to urban centers, have heavily strained cities in recent years. These circumstances have created an ever‐growing need for equipment and essential services. On the other hand, previous research has shown that accessibility measurement is a powerful technique for assessing urban compactness. This assessment arises from the willingness of urban planners to develop transport services and land use across various cities globally. This paper addresses the computational problem of spatial accessibility, focusing on the influence of private cars versus public transport. We introduced a metric that enhances the Balanced Floating Catchment Area (BFCA) index. Our metric not only considers multiple transportation modes in the calculation of spatial accessibility but also takes into account variable catchment sizes. We applied our metric in a case study examining spatial accessibility to public hospitals in Casablanca. The results provide a geographic breakdown of each transportation mode, and the accessibility of different scenarios has been compared.
{"title":"Development of a Variable Multimodal Balanced Floating Catchment Area Approach for Spatial Accessibility Assessment","authors":"Ouidad Benhlima, Fouad Riane, Jakob Puchinger, Hicham Bahi","doi":"10.1111/gean.12398","DOIUrl":"https://doi.org/10.1111/gean.12398","url":null,"abstract":"Rapid urbanization and expansion, stemming from demographic growth and migration from rural areas to urban centers, have heavily strained cities in recent years. These circumstances have created an ever‐growing need for equipment and essential services. On the other hand, previous research has shown that accessibility measurement is a powerful technique for assessing urban compactness. This assessment arises from the willingness of urban planners to develop transport services and land use across various cities globally. This paper addresses the computational problem of spatial accessibility, focusing on the influence of private cars versus public transport. We introduced a metric that enhances the Balanced Floating Catchment Area (BFCA) index. Our metric not only considers multiple transportation modes in the calculation of spatial accessibility but also takes into account variable catchment sizes. We applied our metric in a case study examining spatial accessibility to public hospitals in Casablanca. The results provide a geographic breakdown of each transportation mode, and the accessibility of different scenarios has been compared.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qingyang Fu, Mengjie Zhou, Yige Li, Xiang Ye, Mengjie Yang, Yuhui Wang
Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's I (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity‐based (considering first/higher‐order and common border) and Euclidean distance‐based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long‐tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow‐related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.
{"title":"Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data","authors":"Qingyang Fu, Mengjie Zhou, Yige Li, Xiang Ye, Mengjie Yang, Yuhui Wang","doi":"10.1111/gean.12397","DOIUrl":"https://doi.org/10.1111/gean.12397","url":null,"abstract":"Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's <jats:italic>I</jats:italic> (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity‐based (considering first/higher‐order and common border) and Euclidean distance‐based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long‐tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow‐related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The lack of comprehensive spatial data for neighbourhoods in cities in the global South has posed a significant challenge for examining socio-economic inequities in accessibility to services. By combining the primary (survey data) and secondary data sources with new spatial data sources (Earth observation data, Google Maps), we create a spatial database of 4,145 residential locations in Delhi, aggregating them into 1 km grid-shaped neighbourhoods. The neighbourhood's economic status is evaluated using a composite index of the built environment, land price, and household income. Social characteristics are examined through the percentage of the scheduled caste (SC) population, considering their historical marginalization in Indian society. Using the E-2SFCA method, we calculate accessibility to four key services and employ the geographically weighted regression (GWR) model to explore inequities in accessibility based on neighbourhood location and socio-economic characteristics. Findings reveal inequity in accessibility to services at the neighbourhood level is primarily driven by spatial location rather than income or percentage of SC population. Moreover, the influence of socio-economic characteristics on accessibility varies across locations. The spatial data mapping approach employed in this article can be applied to numerous rapidly urbanizing cities in the global South lacking block or neighbourhood-level spatial data.
{"title":"Analysing Inequity in Accessibility to Services with Neighbourhood Location and Socio-Economic Characteristics in Delhi","authors":"Aviral Marwal, Elisabete A. Silva","doi":"10.1111/gean.12396","DOIUrl":"https://doi.org/10.1111/gean.12396","url":null,"abstract":"The lack of comprehensive spatial data for neighbourhoods in cities in the global South has posed a significant challenge for examining socio-economic inequities in accessibility to services. By combining the primary (survey data) and secondary data sources with new spatial data sources (Earth observation data, Google Maps), we create a spatial database of 4,145 residential locations in Delhi, aggregating them into 1 km grid-shaped neighbourhoods. The neighbourhood's economic status is evaluated using a composite index of the built environment, land price, and household income. Social characteristics are examined through the percentage of the scheduled caste (SC) population, considering their historical marginalization in Indian society. Using the E-2SFCA method, we calculate accessibility to four key services and employ the geographically weighted regression (GWR) model to explore inequities in accessibility based on neighbourhood location and socio-economic characteristics. Findings reveal inequity in accessibility to services at the neighbourhood level is primarily driven by spatial location rather than income or percentage of SC population. Moreover, the influence of socio-economic characteristics on accessibility varies across locations. The spatial data mapping approach employed in this article can be applied to numerous rapidly urbanizing cities in the global South lacking block or neighbourhood-level spatial data.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140073356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：