Geographical Analysis最新文献_第5页

Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations 以随机生成树为弱空间表示的证据累积概率区域化

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-08-23 DOI: 10.1111/gean.12376

Orhun Aydin, Mark V. Janikas, Renato Martins Assunção, Ting-Hwan Lee

{"title":"Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations","authors":"Orhun Aydin, Mark V. Janikas, Renato Martins Assunção, Ting-Hwan Lee","doi":"10.1111/gean.12376","DOIUrl":"10.1111/gean.12376","url":null,"abstract":"Spatial clusters contain biases and artifacts, whether they are defined via statistical algorithms or via expert judgment. Graph-based partitioning of spatial data and associated heuristics gained popularity due to their scalability but can define suboptimal regions due to algorithmic biases such as chaining. Despite the broad literature on deterministic regionalization methods, approaches that quantify regionalization probability are sparse. In this article, we propose a local method to quantify regionalization probabilities for regions defined via graph-based cuts and expert-defined regions. We conceptualize spatial regions as consisting of two types of spatial elements: core and swing. We define three distinct types of regionalization biases that occur in graph-based methods and showcase the use of the proposed method to capture these types of biases. Additionally, we propose an efficient solution to the probabilistic graph-based regionalization problem via performing optimal tree cuts along random spanning trees within an evidence accumulation framework. We perform statistical tests on synthetic data to assess resulting probability maps for varying distinctness of underlying regions and regionalization parameters. Lastly, we showcase the application of our method to define probabilistic ecoregions using climatic and remotely sensed vegetation indicators and apply our method to assign probabilities to the expert-defined Bailey's ecoregions.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"56 2","pages":"328-357"},"PeriodicalIF":3.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48321954","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

Identifying the Impacts of Land-Use Spatial Patterns on Street-Network Accessibility Using Geospatial Methods 利用地理空间方法识别土地利用空间模式对街道网络可达性的影响

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-08-08 DOI: 10.1111/gean.12374

Ping Yu Fan, Kwok Pan Chun, Ana Mijic, Mou Leong Tan, Wei Zhai, Omer Yetemen

{"title":"Identifying the Impacts of Land-Use Spatial Patterns on Street-Network Accessibility Using Geospatial Methods","authors":"Ping Yu Fan, Kwok Pan Chun, Ana Mijic, Mou Leong Tan, Wei Zhai, Omer Yetemen","doi":"10.1111/gean.12374","DOIUrl":"10.1111/gean.12374","url":null,"abstract":"While the land use-street network nexus is well acknowledged, evidence for the one-way impacts of land-use patterns on street accessibility is still inadequate. The measurements of land-use patterns and street accessibility lack systematic knowledge. Their empirical correlations also lack geographical variability, constraining site-specific land-use practices. Therefore, this study overcame the aforementioned limitations by examining the two-level spatial models to formulate accessibility-oriented land plans, using a well-developed Chinese city as an example. Firstly, two landscape metrics—Euclidean Nearest-Neighbor Distance (ENN) and Similarity Index (SIMI)—were used to quantify the intra- and inter-land-use configurations, respectively. Both city-level and local accessibility were measured using spatial design network analysis. Performing both ordinary least squares (OLS) and geographically weighted regression (GWR) models, results identified the statistically significant effects of inter-land-use patterns on two-level street accessibility. An exception was that land-use configurations within residential and industrial regions were irrelevant to street accessibility. We also found GWR was a better-fitting model than OLS when estimating locally-varied accessibility, suggesting hierarchical multiscale land-use planning. Overall, locally heterogeneous evidence in this study can substantialize land use-street network interactions and support the decision-making and implementation of place-specific accessibility-oriented land use.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"56 2","pages":"284-302"},"PeriodicalIF":3.6,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46516062","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

A Framework for Moving Beyond Computational Reproducibility: Lessons from Three Reproductions of Geographical Analyses of COVID-19 超越计算可重复性的框架:来自COVID - 19地理分析的三次再现的教训

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-08-07 DOI: 10.1111/gean.12370

Peter Kedron, Sarah Bardin, Joseph Holler, Joshua Gilman, Bryant Grady, Megan Seeley, Xin Wang, Wenxin Yang

{"title":"A Framework for Moving Beyond Computational Reproducibility: Lessons from Three Reproductions of Geographical Analyses of COVID-19","authors":"Peter Kedron, Sarah Bardin, Joseph Holler, Joshua Gilman, Bryant Grady, Megan Seeley, Xin Wang, Wenxin Yang","doi":"10.1111/gean.12370","DOIUrl":"10.1111/gean.12370","url":null,"abstract":"Despite recent calls to make geographical analyses more reproducible, formal attempts to reproduce or replicate published work remain largely absent from the geographic literature. The reproductions of geographic research that do exist typically focus on computational reproducibility—whether results can be recreated using data and code provided by the authors—rather than on evaluating the conclusion and internal validity and evidential value of the original analysis. However, knowing if a study is computationally reproducible is insufficient if the goal of a reproduction is to identify and correct errors in our knowledge. We argue that reproductions of geographic work should focus on assessing whether the findings and claims made in existing empirical studies are well supported by the evidence presented. We aim to facilitate this transition by introducing a model framework for conducting reproduction studies, demonstrating its use, and reporting the findings of three exemplar studies. We present three model reproductions of geographical analyses of COVID-19 based on a common, open access template. Each reproduction attempt is published as an open access repository, complete with pre-analysis plan, data, code, and final report. We find each study to be partially reproducible, but moving past computational reproducibility, our assessments reveal conceptual and methodological concerns that raise questions about the predictive value and the magnitude of the associations presented in each study. Collectively, these reproductions and our template materials offer a practical framework others can use to reproduce and replicate empirical spatial analyses and ultimately facilitate the identification and correction of errors in the geographic literature.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"56 1","pages":"163-184"},"PeriodicalIF":3.6,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42399487","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

Event Pattern Analysis: Peak Detection and Pattern Comparison 事件模式分析:峰值检测和模式比较

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-07-18 DOI: 10.1111/gean.12372

Yukio Sadahiro

引用次数: 0

Analyzing the Factors that Affect and Predict Employment Density Using Spatial Machine Learning: The Case Study of Seoul, South Korea 利用空间机器学习分析影响和预测就业密度的因素——以韩国首尔为例

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-07-12 DOI: 10.1111/gean.12371

Jane Ahn, Youngsang Kwon

{"title":"Analyzing the Factors that Affect and Predict Employment Density Using Spatial Machine Learning: The Case Study of Seoul, South Korea","authors":"Jane Ahn, Youngsang Kwon","doi":"10.1111/gean.12371","DOIUrl":"10.1111/gean.12371","url":null,"abstract":"There is a regional disparity in the employment density of Seoul. Considering problems such as traffic congestion and jobs-housing imbalance, it is important to understand the spatial pattern of employment density and identify key influencing factors to determine the changes in the future urban spatial structure. This study analyzed employment density in each region of Seoul to derive important predictors. We examined the spatial patterns of employment density and evaluated the effects of spatial and nonspatial factors based on the general model and the spatial heterogeneity model. To predict the distribution of employment density, we used two statistical models (i.e., ordinary least squares regression [OLS] and geographically weighted regression [GWR] models) and two machine learning models (i.e., the random forest [RF] and geographically weighted random forest [GWRF] models). The results showed that the key influencing factors were the number of corporate business companies, number of main and attraction facilities, accessibility to subway stations, areas of commercial and industrial districts, and distance to business districts.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"56 1","pages":"118-142"},"PeriodicalIF":3.6,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47874806","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: 0

The Spatial Autoregressive Panel Data Model with Spatial Moving Average Errors 具有空间移动平均误差的空间自回归面板数据模型

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-07-10 DOI: 10.1111/gean.12369

Chang Tan, J. Paul Elhorst

引用次数: 0

Temporal Network Kernel Density Estimation 时间网络核密度估计

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-06-23 DOI: 10.1111/gean.12368

Jérémy Gelb, Philippe Apparicio

引用次数: 0

Income Segregation Analysis in Limited-Data Contexts: A Methodology Based on Iterative Proportional Fitting 有限数据背景下的收入隔离分析:一种基于迭代比例拟合的方法

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-06-07 DOI: 10.1111/gean.12367

Gonzalo Peraza-Mues, Roberto Ponce-Lopez, Juan Antonio Muñoz Sanchez, Fernanda Cavazos Alanis, Grissel Olivera Martínez, Carlos Brambila Paz

{"title":"Income Segregation Analysis in Limited-Data Contexts: A Methodology Based on Iterative Proportional Fitting","authors":"Gonzalo Peraza-Mues, Roberto Ponce-Lopez, Juan Antonio Muñoz Sanchez, Fernanda Cavazos Alanis, Grissel Olivera Martínez, Carlos Brambila Paz","doi":"10.1111/gean.12367","DOIUrl":"10.1111/gean.12367","url":null,"abstract":"Since the 1950s, researchers in Urban Geography have created multiple instruments for measuring income segregation. However, the computation of such indexes requires the availability of income data and population distribution for small areal units. This approach is problematic for countries and cities where a government's decennial census does not collect or report income data for small-enough areal units to capture income variability within a neighborhood. To address this gap, we use Iterative Proportional Fitting (IPF) to combine neighborhood-level census data with an individual-level income survey data and then estimate small area discrete and continuous income distributions for each small area. We show that it is possible to compute segregation indices based solely on estimated probability distributions without the need to generate a full synthetic population or to obtain integer population counts. We test our empirical method with the case of Mexican cities, for which global and local indexes of segregation are computed with bootstrapped confidence intervals. The major contributions of this article are twofold. First, it uses a method for income-data generation to measure income segregation. Secondly, it demonstrates a linkage between the computation of segregation measures based on probability distributions and the feasibility of computing them directly from the same IPF estimated distributions of income.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"56 1","pages":"79-96"},"PeriodicalIF":3.6,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47135091","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

Multiplant Location Involving Resource Allocation 涉及资源分配的多工厂选址

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-05-31 DOI: 10.1111/gean.12366

Xin Feng

引用次数: 0

The Effects of Weight Choices on the Power of the Getis–Ord Statistic 权重选择对Getis-Ord统计力的影响

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2023-05-11 DOI: 10.1111/gean.12361

Peter Rogerson

引用次数: 0