Geographical Analysis最新文献_第9页

Approximating the Length of Vehicle Routing Problem Solutions Using Complementary Spatial Information 利用互补空间信息近似求解车辆路径问题的长度

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-03-08 DOI: 10.1111/gean.12322

Xi Mei, Kevin M. Curtin, Daniel Turner, Nigel M. Waters, Matthew Rice

{"title":"Approximating the Length of Vehicle Routing Problem Solutions Using Complementary Spatial Information","authors":"Xi Mei, Kevin M. Curtin, Daniel Turner, Nigel M. Waters, Matthew Rice","doi":"10.1111/gean.12322","DOIUrl":"10.1111/gean.12322","url":null,"abstract":"Accurately estimating the length of Vehicle Routing Problem (VRP) distances can inform transportation planning in a wide variety of delivery and service provision contexts. This study extends the work of previous research where multiple linear regression models were used to estimate the average distance of VRP solutions with various customer demands and capacity constraints. This research expands on that approach in two ways: first, the point patterns used in estimation have a wider range of customer clustering or dispersion values as measured by the Average Nearest Neighbor Index (ANNI) as opposed to just using a Poisson or random point process; second, the tour coefficient adjusted by this complementary spatial information is shown to exhibit statistically more accurate estimations. To generate a full range of ANNI values, point patterns were simulated using a Poisson process, a Matern clustering process, and a simple sequential inhibition process to obtain random, clustered, and dispersed point patterns, respectively. The coefficients of independent variables in the models were used to explain how the spatial distributions of customers influence the VRP distances. These results demonstrate that complementary spatial data can be used to improve operational results, a concept that could be applied more broadly.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"125-154"},"PeriodicalIF":3.6,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48737535","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}

引用次数: 1

An Open-Source Model for Projecting Small Area Demographic and Land-Use Change 预测小区域人口和土地利用变化的开源模型

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-02-07 DOI: 10.1111/gean.12320

Nik Lomax, Andrew P. Smith, Luke Archer, Alistair Ford, James Virgo

{"title":"An Open-Source Model for Projecting Small Area Demographic and Land-Use Change","authors":"Nik Lomax, Andrew P. Smith, Luke Archer, Alistair Ford, James Virgo","doi":"10.1111/gean.12320","DOIUrl":"10.1111/gean.12320","url":null,"abstract":"The size, composition, and spatial distribution of both people and households have a substantial impact on the demand for and development and delivery of infrastructure required to support the population. Infrastructure encompasses a wide range of domains including energy, transport, and water, each of which has its own set of spatial catchments at differing scales. Demographic projections are required to assess potential future demand; however, official projections are usually not provided at a high level of spatial resolution required for infrastructure planning. Furthermore, generating bespoke demographic projections, often incorporating a range of scenarios of possible future demographic change is a specialist, resource intensive job and as such is often missing from infrastructure development projects. In this paper we make the case that such demographic projections should be at the heart of infrastructure planning and present a set of open-source models which can be used to undertake this demographic projection work, thus providing the tools needed to fill the identified gap. We make use of a case study for the United Kingdom to exemplify how a range of scenarios can be assessed using our model.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"54 3","pages":"599-622"},"PeriodicalIF":3.6,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63542616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data 分析空间数据的R包:与区域数据的比较案例研究

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-02-06 DOI: 10.1111/gean.12319

Roger Bivand

引用次数: 52

A deeper look at impacts in spatial Durbin model with sphet 用sphet对空间Durbin模型的影响进行更深入的研究

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-01-10 DOI: 10.1111/gean.12318

Gianfranco Piras, Paolo Postiglione

引用次数: 3

A Route Map for Successful Applications of Geographically Weighted Regression 地理加权回归成功应用的路线图

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-01-09 DOI: 10.1111/gean.12316

Alexis Comber, Christopher Brunsdon, Martin Charlton, Guanpeng Dong, Richard Harris, Binbin Lu, Yihe Lü, Daisuke Murakami, Tomoki Nakaya, Yunqiang Wang, Paul Harris

{"title":"A Route Map for Successful Applications of Geographically Weighted Regression","authors":"Alexis Comber, Christopher Brunsdon, Martin Charlton, Guanpeng Dong, Richard Harris, Binbin Lu, Yihe Lü, Daisuke Murakami, Tomoki Nakaya, Yunqiang Wang, Paul Harris","doi":"10.1111/gean.12316","DOIUrl":"https://doi.org/10.1111/gean.12316","url":null,"abstract":"Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"155-178"},"PeriodicalIF":3.6,"publicationDate":"2022-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50142834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recursive Estimation of the Spatial Error Model 空间误差模型的递归估计

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-01-03 DOI: 10.1111/gean.12317

Chiara Ghiringhelli, Gianfranco Piras, Giuseppe Arbia, Antonietta Mira

引用次数: 2

Spatial Causality: A Systematic Review on Spatial Causal Inference 空间因果关系：空间因果推理的系统综述

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-12-14 DOI: 10.1111/gean.12312

Kamal Akbari, Stephan Winter, Martin Tomko

{"title":"Spatial Causality: A Systematic Review on Spatial Causal Inference","authors":"Kamal Akbari, Stephan Winter, Martin Tomko","doi":"10.1111/gean.12312","DOIUrl":"10.1111/gean.12312","url":null,"abstract":"The growing interest in causal inference in recent years has led to new causal inference methodologies and their applications across disciplines and research domains. Yet, studies on spatial causal inference are still rare. Causal inference on spatial processes is faced with additional challenges, such as spatial dependency, spatial heterogeneity, and spatial effects. These challenges can lead to spurious results and subsequently, incorrect interpretations of the outcomes of causal analyses. Recognizing the growing importance of causal inference in the spatial domain, we conduct a systematic literature review on spatial causal inference based on a formal concept mapping. To identify how to assess and control for the adverse effects of spatial influences, we assess publications relevant to spatial causal inference based on criteria relating to application discipline, methods used, and techniques applied for managing issues related to spatial processes. We thus present a snapshot of state of the art in spatial causal inference and identify methodological gaps, weaknesses and challenges of current spatial inference studies, along with opportunities for future research.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"56-89"},"PeriodicalIF":3.6,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49363047","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}

引用次数: 15

In memoriam: Martin Charlton 纪念马丁·查尔顿

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-12-03 DOI: 10.1111/gean.12309

Christopher Brunsdon, Alexis Comber, Paul Harris, Jan Rigby, Andrew Large

引用次数: 0

GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data GeoDa，从桌面到探索空间数据的生态系统

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-11-19 DOI: 10.1111/gean.12311

Luc Anselin, Xun Li, Julia Koschinsky

引用次数: 15

Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis COVID-19期间研究的可重复性:基于空间分析视角的人口密度与基本繁殖率案例检验