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Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data Geogr. Anal. (IF 3.566) Pub Date : 2024-03-12 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
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Analysing Inequity in Accessibility to Services with Neighbourhood Location and Socio‐Economic Characteristics in Delhi Geogr. Anal. (IF 3.566) Pub Date : 2024-03-08 Aviral Marwal, Elisabete A. Silva
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
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Access Weight Matrix: A Place and Mobility Infused Spatial Weight Matrix Geogr. Anal. (IF 3.566) Pub Date : 2024-03-07 Fatemeh Janatabadi, Alireza Ermagun
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
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Delineating Neighborhoods: An Approach Combining Urban Morphology with Point and Flow Datasets Geogr. Anal. (IF 3.566) Pub Date : 2024-03-07 Anirudh Govind, Ate Poorthuis, Ben Derudder
Although neighborhoods are a widely used analytical concept in urban geography, they are often proxied using grids or statistical sectors in empirical research. The rationales underlying these proxies are often separated from the theoretical considerations of what makes a neighborhood a neighborhood, casting shadows over their relevance and applicability. In this article, we identify two specific challenges
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Sub‐Model Aggregation for Scalable Eigenvector Spatial Filtering: Application to Spatially Varying Coefficient Modeling Geogr. Anal. (IF 3.566) Pub Date : 2024-02-29 Daisuke Murakami, Shonosuke Sugasawa, Hajime Seya, Daniel A. Griffith
This study proposes a method for aggregating/synthesizing global and local sub‐models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub‐model, while the generalized product‐of‐experts method was used to aggregate these sub‐models. The major advantages of the proposed
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Spatiotemporal Patterns of Late HIV Diagnosis in Philadelphia at a Small‐area Level, 2011–2016: A Bayesian Modeling Approach Accounting for Excess Zeros Geogr. Anal. (IF 3.566) Pub Date : 2024-02-23 Hui Luan, Yusuf Ransome, Lorraine T. Dean, Tanner Nassau, Kathleen A. Brady
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An Efficient Solving Approach for the p‐Dispersion Problem Based on the Distance‐Based Spatially Informed Property Geogr. Anal. (IF 3.566) Pub Date : 2024-02-22 Changwha Oh, Hyun Kim, Yongwan Chun
The p‐dispersion problem is a spatial optimization problem that aims to maximize the minimum separation distance among all assigned nodes. This problem is characterized by an innate spatial structure based on distance attributes. This research proposes a novel approach, named the distance‐based spatially informed property (D‐SIP) method to reduce the problem size of the p‐dispersion instances, facilitating
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Measuring and Testing Multivariate Spatial Autocorrelation in a Weighted Setting: A Kernel Approach Geogr. Anal. (IF 3.566) Pub Date : 2024-02-13 François Bavaud
We propose and illustrate a general framework in which spatial autocorrelation is measured by the Frobenius product of two kernels, a feature kernel and a spatial kernel. The resulting autocorrelation index δ$$ \delta $$ generalizes Moran's index in the weighted, multivariate setting, where regions, differing in importance, are characterized by multivariate features. Spatial kernels can traditionally
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Balancing Workloads through Co-location in Covering Problems Geogr. Anal. (IF 3.566) Pub Date : 2024-01-31 Jing Xu, Alan T. Murray, Richard L. Church, Ran Wei, Hongchu Yu, Jiwon Baik, Enbo Zhou
Total demand suitably served and facility workload balance are two important considerations in location coverage. Previous work has dealt with workload balancing issues using a number of approaches, including imposing facility capacities and the use of multiple objectives focused on workload variation. However, a facility is usually restricted to a single service unit, inconsistent with strategies
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Population and Morphological Change: A Study of Building Type Replacements in the Osaka-Kobe City-Region in Japan Geogr. Anal. (IF 3.566) Pub Date : 2024-01-19 Joan Perez, Giovanni Fusco, Yukio Sadahiro
As cities adapt to new needs and challenges, their forms change in close relation to population dynamics. This article focuses on the link between population dynamics and the evolution of building hull types. The case study is the Osaka-Kobe city-region in Japan, a country globally witnessing an intense population decline. Morphometric indicators are coupled with a tree-like classificatory model in
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Hybridizing Geographically Weighted Regression and Multilevel Models: A New Approach to Capture Contextual Effects in Geographical Analyses Geogr. Anal. (IF 3.566) Pub Date : 2024-01-10 Thierry Feuillet, Etienne Cossart, Helene Charreire, Arnaud Banos, Hugo Pilkington, Virginie Chasles, Serge Hercberg, Mathilde Touvier, Jean Michel Oppert
Multilevel models are one of the main statistical methods used in modeling contextual effects in social sciences. A common limitation of these methods is the use pre-set boundaries—usually administrative units—to define contexts, when these boundaries do not always match up with the “true” causally relevant contexts that may affect the outcomes of interest. In this study applied to the obesity geography
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The Geographical Analysis of Megacities Through Changes in Their Individual Urban Objects Geogr. Anal. (IF 3.566) Pub Date : 2024-01-02 Xiangning Fan, George Alan Blackburn, James Duncan Whyatt, Peter Michael Atkinson
This research utilized global coverage, annual, high-quality land cover time-series data to explore the urban growth process in the core area, and in several buffer zones, of Beijing, Guangzhou, Shanghai, and Tokyo. We developed a conceptual model in which growth is characterized at the per-object level by four active growth events: introduction, establishment, dispersal, and coalescence, with a fifth
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Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator Geogr. Anal. (IF 3.566) Pub Date : 2023-12-22 Elisa Fusco, Matheus Pereira Libório, Hamidreza Rabiei-Dastjerdi, Francesco Vidoli, Chris Brunsdon, Petr Iakovlevitch Ekel
Spatially heterogeneous weights and a non-compensatory aggregation scheme, are two important properties needed to construct a composite indicator capable of summarizing properly the multidimensional phenomenon of local spatial units. Such a composite indicator takes into account, on the one hand, the latent characteristics of the specific units related to their location in the territory, and on the
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Valeriepieris Circles for Spatial Data Analysis Geogr. Anal. (IF 3.566) Pub Date : 2023-12-13 Rudy Arthur
The Valeriepieris (VP) circle is the smallest circle containing half of the world's population. The Valeriepieris circle acts as a spatial median, splitting spatial data into two halves in a unique way. In this article the idea of the VP circle is generalized and a fast algorithm to compute it is described. This algorithm has been implemented in Python and is available for download and use. The VP
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Spatiotemporal Variograms as Neighborhood Definers Geogr. Anal. (IF 3.566) Pub Date : 2023-12-12 Brendan J Hurley, Timothy F Leslie
Spatial neighborhood definitions are a consistent source of disagreement among geographic scholars. This research will focus on the implementation and evaluation of spatiotemporal variograms (STVs) as a source of spatial neighborhood definition. STVs show the similarity, measured by semivariance, of spatial events to each other when separated by time and space. Over both time and space, there should
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Measuring Spatial Dispersion: An Experimental Test on the M-Index Geogr. Anal. (IF 3.566) Pub Date : 2023-11-28 Alberto Tidu, Frederick Guy, Stefano Usai
Despite representing a very accurate method for assessing spatial distribution, Marcon and Puech's M has been insufficiently exploited so far, most likely because its computation relies on pairing every point of interest (i.e., firms, plants) with every other point within the area under analysis. Such a figure rapidly grows to unmanageable levels when said area is larger than a neighborhood or when
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Spatial Patterns in the Association between the Prevalence of Asthma and Determinants of Health Geogr. Anal. (IF 3.566) Pub Date : 2023-11-02 Carmen Bentué-Martínez, Marcos Rodrigues, José María Llorente González, Antonio Sebastián Ariño, Marcos Zuil Martínez, María Zúñiga-Antón
The World Health Organization endorses the study of diseases from the perspective of the Determinants of Health (DH), that is, the circumstances in which people are born and raised, the environment in which they grow up and age and their lifestyle. The aim of this study is to analyze the spatial behavior of the prevalence of asthma in Aragon, a Mediterranean region in Spain, under the DH approach.
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Relative Variability in Streetscape Skeletons and Spatial Association: Application for Identifying Harmonious and Inharmonious Streetscape Skeletons in Tokyo Geogr. Anal. (IF 3.566) Pub Date : 2023-10-09 Hiroyuki Usui
Whether or not a streetscape skeleton (defined as the 3D street space) is harmonious depends on the degree of difference between heights and setbacks of adjacent buildings, which is called the relative variability in the streetscape skeleton, but this has generally been overlooked. Because streetscape skeletons are ambiguous, evaluating whether or not they are harmonious is thus conceptually and technically
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Understanding Changes in Spatial Accessibility to Restaurants During the COVID-19 Pandemic: Disentangling Closures, Inequity, Neighborhood, and Transportation Mode Geogr. Anal. (IF 3.566) Pub Date : 2023-10-06 Kyusik Kim, Mark W. Horner, Md. Shaharier Alam, Onur Alisan, Mahyar Ghorbanzadeh, Eren Erman Ozguven
Among one of the more significant societal impacts of the COVID-19 pandemic, restrictions on people's movement accelerated, and in some cases outright caused, restaurant closures. By considering people's potential for both driving and walking to restaurants, this study aims to examine how restaurant closures are associated with neighborhood characteristics during the pandemic. To do so, we investigated
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What's a School Worth to a Neighborhood? A Spatial Hedonic Analysis of Property Prices in the Context of Accommodation Reviews in Ontario Geogr. Anal. (IF 3.566) Pub Date : 2023-09-19 John Merrall, Christopher D. Higgins, Antonio Paez
Due to a change in capital funding formula, many school boards across the Province of Ontario engaged in Accommodation Reviews to rationalize the supply of school capacity. This process led to numerous school closures and raised important policy questions regarding the economic value of a school in terms of its capitalization into property values and, by extension, how the closure of a school might
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Multiscale Continuous and Discrete Spatial Heterogeneity Analysis: The Development of a Local Model Combining Eigenvector Spatial Filters and Generalized Lasso Penalties Geogr. Anal. (IF 3.566) Pub Date : 2023-09-12 Zhan Peng, Ryo Inoue
Two types of spatial heterogeneity can exist simultaneously: continuous variations across an entire space and significant changes that occur only in specific spatial units. Moreover, each of these can act across multiple spatial scales. To effectively detect both continuous and discrete spatial heterogeneity across different scales, this study proposes a novel approach that combines the random effects
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Conceptualizing Urban Inequalities as a Complex Socio-Technical Phenomenon Geogr. Anal. (IF 3.566) Pub Date : 2023-09-12 Ruth Nelson, Martijn Warnier, Trivik Verma
The United Nations World Social Report (2020) reveals that more than two thirds of the world's population live in countries where urban inequalities have increased in the last three decades. While urban inequalities are traditionally characterized as an economic issue, scholars are increasingly applying methods from geospatial analysis to study them. In the context of these advancements, it remains
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Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations Geogr. Anal. (IF 3.566) Pub Date : 2023-08-23 Orhun Aydin, Mark V. Janikas, Renato Martins Assunção, Ting-Hwan Lee
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
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Identifying the Impacts of Land-Use Spatial Patterns on Street-Network Accessibility Using Geospatial Methods Geogr. Anal. (IF 3.566) Pub Date : 2023-08-08 Ping Yu Fan, Kwok Pan Chun, Ana Mijic, Mou Leong Tan, Wei Zhai, Omer Yetemen
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
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A Framework for Moving Beyond Computational Reproducibility: Lessons from Three Reproductions of Geographical Analyses of COVID-19 Geogr. Anal. (IF 3.566) Pub Date : 2023-08-07 Peter Kedron, Sarah Bardin, Joseph Holler, Joshua Gilman, Bryant Grady, Megan Seeley, Xin Wang, Wenxin Yang
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
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Event Pattern Analysis: Peak Detection and Pattern Comparison Geogr. Anal. (IF 3.566) Pub Date : 2023-07-18 Yukio Sadahiro
This article proposes two exploratory methods for analyzing event patterns. Events in this article refer to zero-dimensional objects in the spatiotemporal dimension, such as crimes, earthquakes, and traffic accidents. One method detects the peaks in event patterns, evaluates the degree of event concentration at the peaks, and visualizes its spatial variation. Another method evaluates the similarity
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Analyzing the Factors that Affect and Predict Employment Density Using Spatial Machine Learning: The Case Study of Seoul, South Korea Geogr. Anal. (IF 3.566) Pub Date : 2023-07-12 Jane Ahn, Youngsang Kwon
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
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The Spatial Autoregressive Panel Data Model with Spatial Moving Average Errors Geogr. Anal. (IF 3.566) Pub Date : 2023-07-10 Chang Tan, J. Paul Elhorst
This paper advocates the wider use of the spatial autoregressive (AR) panel data model with spatial moving average (MA) errors, individual and time effects, and different spatial weight matrices for each spatial lag. We demonstrate the practical relevance of this model, derive and investigate the asymptotic properties of a simple quasi maximum likelihood within estimator when N$$ N $$ is large and
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Temporal Network Kernel Density Estimation Geogr. Anal. (IF 3.566) Pub Date : 2023-06-23 Jérémy Gelb, Philippe Apparicio
Kernel density estimation (KDE) is a widely used method in geography to study concentration of point pattern data. Geographical networks are 1.5 dimensional spaces with specific characteristics, analyzing events occurring on networks (accidents on roads, leakages of pipes, species along rivers, etc.). In the last decade, they required the extension of spatial KDE. Several versions of Network KDE (NKDE)
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Income Segregation Analysis in Limited-Data Contexts: A Methodology Based on Iterative Proportional Fitting Geogr. Anal. (IF 3.566) Pub Date : 2023-06-07 Gonzalo Peraza-Mues, Roberto Ponce-Lopez, Juan Antonio Muñoz Sanchez, Fernanda Cavazos Alanis, Grissel Olivera Martínez, Carlos Brambila Paz
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
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Multiplant Location Involving Resource Allocation Geogr. Anal. (IF 3.566) Pub Date : 2023-05-31 Xin Feng
Recently, a multisource, raw material allocation form of Weber's classic single-facility location problem was rediscovered and recognized for its significance in contemporary planning and decision-making. This variation of the Weber problem investigates the location of a production plant while permitting the selection of each required raw material source. This article reviews the Weber problem with
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The Effects of Weight Choices on the Power of the Getis–Ord Statistic Geogr. Anal. (IF 3.566) Pub Date : 2023-05-11 Peter Rogerson
When local spatial clustering exists, local statistics are most likely to be significant when their associated weights match the spatial form and extent of the actual clustering. This paper focuses upon the cost of misspecifying the weights of the Getis–Ord statistic. In particular, it is more difficult to reject false null hypotheses when the weights are poorly chosen. I also examine the likelihood
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Erratum for ‘Delineating the Spatio-Temporal Pattern of House Price Variation by Local Authority in England: 2009 to 2016’ by Chi et al. (2021) Geogr. Anal. (IF 3.566) Pub Date : 2023-04-18
In Chi et al. (2021), there was an error occurred in Abstract due to a production error. The word ‘80-year’ in the Abstract has been corrected to ‘8-year’, this error has been corrected in the article.
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Ranking Spatial Units with Structural Property and Traffic Distributions for Uncovering Spatial Interaction Patterns in a City Geogr. Anal. (IF 3.566) Pub Date : 2023-03-22 Wenhao Yu, Yi-fan Zhang, Mengqi Liu, Chuncheng Yang, Xiao Wu
Travel activity data mining is critical to numerous urban applications such as transportation and location-based services. This article studies the spatial units ranking algorithm for uncovering spatial interaction patterns based on the flow properties of people's travel trajectories. For example, using a taxi origin–destination flow database, a user may want to rank the origin and destination with
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The Art of Geographical Analysis Geogr. Anal. (IF 3.566) Pub Date : 2023-03-03 Alan T. Murray
Professor Arthur Getis was a prominent geographical analysis researcher and proponent. His research in geographical analysis was broad, with an eye on theoretical developments and application-oriented details. However, there was so much more. His active participation and engaged discussion at symposia and conferences, in sessions, during breaks and less formally over drinks or a meal, stand out, even
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Generalizing Impact Computations for the Autoregressive Spatial Interaction Model Geogr. Anal. (IF 3.566) Pub Date : 2023-03-01 Thibault Laurent, Paula Margaretic, Christine Thomas-Agnan
We extend the impact decomposition proposed by LeSage and Thomas-Agnan (2015) in the spatial interaction model to a more general framework, where the sets of origins and destinations can be different, and where the relevant attributes characterizing the origins do not coincide with those of the destinations. These extensions result in three flow data configurations which we study extensively: the square
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A Comparison of Spatial and Nonspatial Methods in Statistical Modeling of NO2: Prediction Accuracy, Uncertainty Quantification, and Model Interpretation Geogr. Anal. (IF 3.566) Pub Date : 2023-01-17 Meng Lu, Joaquin Cavieres, Paula Moraga
NO 2 $$ {\mathrm{NO}}_2 $$ is a traffic-related air pollutant. Ground NO 2 $$ {\mathrm{NO}}_2 $$ monitoring stations measure NO 2 $$ {\mathrm{NO}}_2 $$ concentrations at certain locations and statistical predictive methods have been developed to predict NO 2 $$ {\mathrm{NO}}_2 $$ as a continuous surface. Among them, ensemble tree-based methods have shown to be powerful in capturing nonlinear relationships
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Understanding Population Decline Trajectories in Spain using Sequence Analysis Geogr. Anal. (IF 3.566) Pub Date : 2023-01-16 Miguel González-Leonardo, Niall Newsham, Francisco Rowe
Population decline is a key contemporary demographic challenge. Previous work has measured the national extent of population decline, and we know that it is more acute in Japan and Eastern Europe and is set to accelerate across many industrialized countries. Yet, little is known about the population trajectories leading to current trends of depopulation and their underpinning demographic and contextual
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Corrigendum Geogr. Anal. (IF 3.566) Pub Date : 2022-12-05
The data used in this study were derived from a standard data product provided by Baidu Huiyan (huiyan.baidu.com). It recently came to the knowledge of the author group that a substantial description of the original data product had been mistaken. Although the error did not impact the results and conclusions we reported, it would still probably mislead our readers. The author group apologizes for this
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Prediction of Bike-sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm Geogr. Anal. (IF 3.566) Pub Date : 2022-11-29 Katja Schimohr, Philipp Doebler, Joachim Scheiner
Regression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike-sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to account for spatial autocorrelation. Independent variables
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Comparison of Moran's I and Geary's c in Multivariate Spatial Pattern Analysis Geogr. Anal. (IF 3.566) Pub Date : 2022-11-25 Jie Lin
This article compares multivariate spatial analysis methods that include not only multivariate covariance, but also spatial dependence of the data explicitly and simultaneously in model design by extending two univariate autocorrelation measures, namely Moran's I and Geary's c. The results derived from the simulation datasets indicate that the standard Moran component analysis is preferable to Geary
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Testing Transferability: Quantitative Evaluation of Labor Market Area Definition Methods in Three Contrasting Countries Geogr. Anal. (IF 3.566) Pub Date : 2022-11-10 José Manuel Casado-Díaz, Mike Coombes, Lucas Martínez-Bernabéu
Sub-national economic policies increasingly use labor market areas (LMAs) rather than administrative areas for analysis and implementation. How a set of LMAs was defined influences the results of such analyses, and so accurate policy delivery needs appropriately defined LMAs. Multinational bodies need comparable LMA definitions in many countries, calling for a definition method that is transferable
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A Rejoinder to the Commentaries on “A Route Map for Successful Applications of Geographically Weighted Regression” by Comber et al. (2022) Geogr. Anal. (IF 3.566) Pub Date : 2022-11-05 Alexis Comber, Paul Harris, Chris Brunsdon
1 Preamble: The route map 1.1 What the RM says In brief, the GWR Route Map (RM) by Comber et al. (2022a) argues that an ordinary least squares (OLSs) regression and a multiscale GWR should always be undertaken initially. Then, by examining the outputs and results of these, the final choice of model can be determined by applying some very broad rubrics. 1.2 Why we wrote the RM We wrote the RM for two
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A Simulation Study to Explore Inference about Global Moran's I with Random Spatial Indexes Geogr. Anal. (IF 3.566) Pub Date : 2022-10-17 René Westerholt
Inference procedures for spatial autocorrelation statistics assume that the underlying configurations of spatial units are fixed. However, sometimes this assumption can be disadvantageous, for example, when analyzing social media posts or moving objects. This article examines for the case of point geometries how a change from fixed to random spatial indexes affects inferences about global Moran's I
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A Hybrid Approach for Mass Valuation of Residential Properties through Geographic Information Systems and Machine Learning Integration Geogr. Anal. (IF 3.566) Pub Date : 2022-10-14 Muhammed Oguzhan Mete, Tahsin Yomralioglu
Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation
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Three Common Machine Learning Algorithms Neither Enhance Prediction Accuracy Nor Reduce Spatial Autocorrelation in Residuals: An Analysis of Twenty-five Socioeconomic Data Sets Geogr. Anal. (IF 3.566) Pub Date : 2022-10-13 Insang Song, Daehyun Kim
Machine learning (ML) is being applied in an increasing volume of geographical research. However, the aspects of spatial autocorrelation (SAC) in the residuals produced by ML models have been understudied compared to the benefit of ML, namely, reduction of prediction errors. In this study, we examined the relationship between predictive accuracy and the reduction in the residual SAC for 597 variables
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City Size Distribution Analyses Based on the Concept of Entropy Competition Geogr. Anal. (IF 3.566) Pub Date : 2022-10-02 Antonio Sanchirico, Giovanna Andrulli, Mauro Fiorentino
The present work pursues theoretical and empirical objectives. With regards to the former, it is demonstrated that the natural tendency to uniformity of both the probability distribution of a city to have a certain number of inhabitants and that of a person to reside in a town of a given number of citizens leads to a competition between their information entropies, which provides the power law distribution
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Navigating the Methodological Landscape in Spatial Analysis: A Comment on “A Route Map for Successful Applications of Geographically-Weighted Regression” Geogr. Anal. (IF 3.566) Pub Date : 2022-09-17 Taylor M. Oshan
The development of “route maps” for spatial analytical methods is a pursuit with important ramifications. Comber et al. propose a route map to guide applications of geographically weighted regression consisting of a three-step primary pathway and a series of secondary arterials. This comment first highlights some concerns about the underlying “map” (i.e., experimental setup and assumptions) and then
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The Right to Rule by Thumb: A Comment on Epistemology in “A Route Map for Successful Applications of Geographically-Weighted Regression” Geogr. Anal. (IF 3.566) Pub Date : 2022-09-15 Levi John Wolf
Comber et al. provide an important contribution to the future of quantitative geography and Geographical Analysis. The contribution is chiefly in their development of a “GWR Route Map,” a diagram showing the sequence of analytical steps that “successful” specification searches in local modeling tend to follow. Geographically weighted techniques have been rapidly expanding, both in terms of complexity
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Local Indicator of Spatial Agglomeration between Newly Opened Outlets and Existing Competitors on a Street Network Geogr. Anal. (IF 3.566) Pub Date : 2022-08-21 Wataru Morioka, Mei-Po Kwan, Atsuyuki Okabe, Sara L. McLafferty
Distance from competitors is a key factor in retail site selection and profitability. To understand the locational tendency that each newly opened outlet locates close to or far from existing competitors in a target area, a specific method is needed. Hence, this study aims first to develop a statistical method to discover the local spatial associations between newly opened and existing point-like outlets
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Effects of Confidentiality-Preserving Geo-Masking on the Estimation of Semivariogram and of the Kriging Variance Geogr. Anal. (IF 3.566) Pub Date : 2022-08-18 Giuseppe Arbia, Chiara Ghiringhelli, Vincenzo Nardelli
Geostatistical methods, such as semivariograms and kriging are well-known spatial tools commonly employed in many disciplines such as health, mining, forestry, meteorology to name only few. They are based essentially on point-referenced data on a continuous space and on the calculation of distances between them. In many practical instances, however, the exact point location, even if exactly known,
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Spatial Search and Bayes Theorem: A Commentary on Recent Examples from Aircraft Accidents Geogr. Anal. (IF 3.566) Pub Date : 2022-08-17 Morton E. O'Kelly
This paper presents a Bayesian search methodology in the context of missing aircraft, as well as a few other related search operations. The search seeks an item hidden in one of n cells. The parameters controlling the search are the prior probabilities (updated during each phase of the search) and the search quality. Assume the search begins in the area with the maximal prior. The expected length of
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The Spatiotemporal Evolution of Sydney's Tram Network Using Network Econometrics Geogr. Anal. (IF 3.566) Pub Date : 2022-07-17 Yingshuo Wang, Bahman Lahoorpoor, David M. Levinson
This paper examines the evolution of Sydney trams using network econometrics approaches. Network econometrics extends spatial econometrics by developing weight matrices based on the physical structure of the network, allowing for competing and complementary elements to have distinct effects. This research establishes a digitized database of Sydney historical tramway network, providing a complete set
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An Examination of the Stochastic Distribution of Spatial Accessibility to Intensive Care Unit Beds during the COVID-19 Pandemic: A Case Study of the Greater Houston Area of Texas Geogr. Anal. (IF 3.566) Pub Date : 2022-07-09 Jinwoo Park, Daniel W. Goldberg
Sufficient and reliable health care access is necessary for people to be able to maintain good health. Hence, investigating the uncertainty embedded in the temporal changes of inputs would be beneficial for understanding their impact on spatial accessibility. However, previous studies are limited to implementing only the uncertainty of mobility, while health care resource availability is a significant
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A Framework for Inserting Visually Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research Geogr. Anal. (IF 3.566) Pub Date : 2022-07-06 Roger Beecham, Robin Lovelace
Road safety research is a data-rich field with large social impacts. Like in medical research, the ambition is to build knowledge around risk factors that can save lives. Unlike medical research, road safety research generates empirical findings from messy observational datasets. Records of road crashes contain numerous intersecting categorical variables, dominating patterns that are complicated by
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A Multi-objective Optimization Approach for Disaggregating Employment Data Geogr. Anal. (IF 3.566) Pub Date : 2022-07-01 Chantel Ludick, Quintin van Heerden
In many countries, including South Africa, data on employment is rarely available on a downscaled level, such as building level, and is only available on less detailed levels, such as municipal level. The aim of this research was to develop a methodology to disaggregate the employment data that is available at an aggregate level to a disaggregate, detailed building level. To achieve this, the methodology
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Open Source Software for Spatial Data Science Geogr. Anal. (IF 3.566) Pub Date : 2022-06-28 Luc Anselin, Sergio J. Rey
Much progress has been made in the development of software tools for spatial analysis since the special issue of Geographical Analysis appeared in 2006, devoted to “Recent advances in software for spatial analysis in the social sciences” (Rey and Anselin 2006). The 15 some years since the publication of the issue have been marked by major changes in the spatial analytical software landscape. Arguably
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gwverse: A Template for a New Generic Geographically Weighted R Package Geogr. Anal. (IF 3.566) Pub Date : 2022-06-28 Alexis Comber, Martin Callaghan, Paul Harris, Binbin Lu, Nick Malleson, Chris Brunsdon
GWR is a popular approach for investigating the spatial variation in relationships between response and predictor variables, and critically for investigating and understanding process spatial heterogeneity. The geographically weighted (GW) framework is increasingly used to accommodate different types of models and analyses, reflecting a wider desire to explore spatial variation in model parameters
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Effects of Vaccination and the Spatio-Temporal Diffusion of Covid-19 Incidence in Turkey Geogr. Anal. (IF 3.566) Pub Date : 2022-06-04 Firat Bilgel, Burhan Can Karahasan
This study assesses the spatio-temporal impact of vaccination efforts on Covid-19 incidence growth in Turkey. Incorporating geographical features of SARS-CoV-2 transmission, we adopt a spatial Susceptible–Infected–Recovered (SIR) model that serves as a guide of our empirical specification. Using provincial weekly panel data, we estimate a dynamic spatial autoregressive (SAR) model to elucidate the
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Big Code Geogr. Anal. (IF 3.566) Pub Date : 2022-06-04 Sergio J. Rey
Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects