Air pollution is of increasing concern to urban residents and urban planners are struggling to find interventions which tackle the trade‐off between environmental, health, and economic impacts arising from this. We analyze within a spatially explicit theoretical residential choice model how different urban interventions can reduce exposure to endogenous traffic‐induced air pollution at residential

Geographically weighted regression model with a spatially autoregressive term of the response variable (GWR‐Lag model for short) is a useful tool to simultaneously model spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. The profile maximum likelihood estimation has been developed to calibrate the model, where a single bandwidth is used for all

Housing is a major source of inequality in England, but most house price variation studies are conducted at national or regional scale or, conversely, in a specific city. Detailed research at sub‐regional level is missing, especially for the period after the global financial crisis. This research addresses this gap with an analysis of variation at local authority level across England between 2009 and

Nearest‐neighbor analysis (NNA)—a method for assessing the degree to which a spatial point pattern departs from randomness in the direction of being either clustered or regular—was imported into academic geography from an article published in 1954 by ecologists Clark and Evans. In its simplest form, concerned with distances to the first nearest neighbor, NNA hinged on the behavior of the “nearest‐neighbour

The use of location models in retail businesses is well‐established, particularly in the grocery sector. Many alternative methods are in use today but the spatial interaction model (SIM) has a proven record of success. To date, that success relates purely to face‐to‐face activities, modeling and predicting visits by consumers to retail outlets. However, grocery retailers are cutting back on store investments

Many studies have demonstrated that collective efficacy is associated with positive health outcomes, lower crime, and violence in urban communities, and residents’ emotional connection to their community. Remediation of blighted properties has been theoretically linked to increases in collective efficacy. The purpose of this study was to examine the impact of blighted property remediation on city non‐emergency

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of every urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open‐source OSMnx software. In total

Despite several attempts to compare and examine the predictive accuracy of real estate sales and rent prices between the regression‐based and neural‐network (NN)‐based approaches, the results are largely mixed. Prior study limitations include a small sample size and a disregard for spatial dependence, which is an essential characteristic of real estate properties. Hence, this study aims to add new

Spatial data regularly suffer from error and uncertainty, ranging from poorly georeferenced coordinate pairs to sampling error associated with American Community Survey data. Geographic information systems can amplify and propagate error and uncertainty through the abstraction and representation of spatial data, as can the manipulation, processing, and analysis of spatial data using exploratory and

Map comparisons in three‐dimensional space (3D) and 3D time series (4D) are becoming a necessity with increased availability of multidimensional data and model simulation outputs. Therefore, this research study extends the two‐dimensional (2D) map comparison methods with the aim to propose a suite of 3D approaches such as 3D Kappa, 3D Fuzzy, and 4D Fuzzy Kappa coefficients specifically designed to

Identifying spatial communities in massive vehicle trajectory data greatly facilitates the understanding of spatial interactions in a city. However, it is still challenging to identify irregularly shaped and statistically significant spatial communities in vehicle movements. To overcome this challenge, we develop a spatial scan statistic based on ant colony optimization. A spatially embedded network

Bipartite projections have become a common way to measure spatial networks. They are now used in many subfields of geography, and are among the most common ways to measure the world city network, where intercity links are inferred from firm co‐location patterns. Bipartite projections are attractive because a network can be indirectly inferred from readily available data. However, spatial bipartite

Space‐time accessibility usually denotes the ability or potential to reach some destinations of value according to some given spatial and temporal constraints. While most of space‐time accessibility models so far recognize that space and time dimensions constrain the way humans might perform some activities at some given locations, additional semantics such as the ones exhibited by points of interest

Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent‐based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual‐level data and computing power have opened up

This article presents the first systematic study of the spatial transmission of the 1929–33 epidemic of meningococcal meningitis and its association with local coal mining communities in three adjacent high incidence counties of England; Nottinghamshire, Derbyshire, and the West Riding of Yorkshire. Drawing on a robust method of spatial epidemiological analysis (swash‐backwash model), we demonstrate

An omission error occurs when independent variables are missing from a regression model. When individual observations are not available, the modifiable areal unit problem (MAUP) appears with spatially aggregated data sets. Both omission error and the MAUP can occur simultaneously in regression analyses. In particular, the MAUP causes the bias due to an omission error to be less predictable for linear

The increasing use of “new” machine learning techniques, such as random forest, provides an impetus to researchers to better understand the role of space in these models. Thus, this article develops an approach for constructing spatially explicit random forest models by including spatially lagged variables to mirror various spatial econometric specifications in order to test their comparative performance

The detection of colocation pattern is an important and widely used method to analyze the spatial associations of geographical objects and events. Existing studies primarily focus on discovering colocation patterns and association rules based on point data. A broad range of flow data types, such as population flow, logistics, and information flow, have emerged in recent years. However, colocation patterns

The paper provides numerical measures and visualizations of urban segregation based on a new index, the Distortion coefficient. Distortion coefficients are derived from trajectories of contact with the city’s population as an individual will encounter an increasing number of persons in a growing distance from their original location. They can be interpreted as measures of how different, or in technical

Common techniques for approximating the maximum likelihood estimator often lead to biased and inconsistent estimates when a systematic bias is introduced into the log‐likelihood function. This article proposes to replace the true scores in the likelihood equations with quasi‐scores that are easy‐to‐compute yet have an expected value of zero. This innovation would dramatically simplify the likelihood‐based

The analysis of correlation structures among multivariate spatially aggregated data has become increasingly important and poses substantial challenges. This article concerns the development of partial and semi‐partial statistics of spatial associations in the context of multivariate spatial areal data extending Moran's I and Geary's C. The proposed statistical tools describe global or local associations

City planners have a professional and ethical responsibility to provide public goods equitably. Parks improve mental and physical health by nurturing social cohesion and enabling physical activity. So who gets parks? Park access has traditionally been evaluated using constructed variables of potential access: distances, buffers, and gravity models. These models have major limitations: they ignore commutes

Dengue disease has serious health and socioeconomic consequences. Preventive actions are needed to avoid outbreaks. Bayesian spatiotemporal models with conditional autoregressive (CAR) and random walk (RW) priors are two common smoothing approaches used in disease mapping to develop early warning systems and action plans. However, this approach can lead to over‐smoothing, such that discontinuities

The paper proposes a new version of spatial shift‐share decomposition to improve on the various approaches to conventional shift‐share analysis found in the literature. The novelty of our proposal is that it enables researchers to assess spatial competitiveness effects controlling for the influence of industrial specialization at both regional and neighborhood level. This new version is applied to

Our goal is to establish a mathematical framework for the description of geographical distance in a comprehensive way. Geographical distance always refer to potential or realized movement between places, and these displacements obey the least effort rule. While this optimization of effort is well known to imply the Triangle Inequality in many situation, breaks in movement generate a paradox: effort

This review is based on a plenary lecture delivered at a recent annual meeting of the American Association of Geographers, sponsored by the Spatial Analysis and Modeling specialty group. This group began around 1979 as Mathematical Models & Quantitative Methods, with a subsequent name change in 2000 to better reflect the maturing interests and concerns of members. Some twenty years down the road, advances

This article contributes to advancing the knowledge on the phenomenon of the most popular short‐term rental platforms, Airbnb. By implementing a geographically weighted regression (GWR) and its multiscale form, MGWR, we examine the relationship between Airbnb locations and the core elements of urban tourism including hotels, food and beverages (F&B) venues, as well as access to public transport. This

The academic contributions of Waldo Tobler are noteworthy and significant, spanning essentially all disciplines that involve the study of geographic phenomena. While much attention has been given to his observations of the first law of geography, there is much more substance to his larger body of research. It is especially fitting that this commemorative special issue is appearing in Geographical Analysis

Distance is an important and basic concept in geography. Many theories, methods, and applications involve distance explicitly or implicitly. While measuring the distance between two locations is a straightforward task, many geographical processes involve areal units, where the distance measurement can be complicated. This research investigates distance measurement between a location (point) and an

North American bat species have been undergoing extreme population declines due to the White‐Nose Syndrome (WNS) epidemic caused by the spread of its pathogen, Pseudogymnoascus destructans. Existing models that represent the spread of the disease are limited in their scalability for use in management decisions or lacked the sophistication necessary to capture the complexity of WNS spread. Grounded

For the study of spatially distributed phenomena, standardizing count data by underlying populations facilitates meaningful comparison across different times and places. However, significant barriers to such standardization are faced by researchers studying small geographic areas, where the capacity for collecting and reporting demographic information is limited. These challenges may include population

Distance is the most fundamental metric in spatial analysis and modeling. Planar distance and geodesic distance are the common distance measurements in current geographic information systems and geospatial analytic tools. However, there is little understanding about how to measure distance in a digital terrain surface and the uncertainty of the measurement. To fill this gap, this study applies a Monte‐Carlo

This article applies a method we term “predictive clustering” to cluster neighborhoods. Much of the literature in this direction is based on groupings built using intrinsic characteristics of each observation. Our approach departs from this framework by delineating clusters based on how the neighborhood’s features respond to a particular outcome of interest (e.g., income change). To do so, we leverage

The majority of segregation studies focus on ethnic concentration but there is growing research that also documents high and increasing status segregation. While empirical studies have documented the existence of both ethnic concentration and status segregation, there is only limited research on the two complexly related distributions. In this article, we examine the conjoint relationship of ethnic

In this commentary, I reflect on geographical analysis as it enters middle age, focusing on what I perceive to be central elements—for both field and journal—of past growth and development, as well as future robustness and potential. My particular interest lies in evaluating “geographical analysis” as it stands today, taking the journal as one proxy for the larger field, and placing this within a wider

Waldo Tobler introduced his hiking function in a little‐known article in 1993, as a formal representation of the relationship between topographic gradient and the velocity of an average hiker, with parameters estimated from some previously collected data. Such functions are becoming more useful as apps for route guidance proliferate. Numerous practical issues are raised by any effort to validate these

The opponents of big‐box entry argue that large retail establishments generate a variety of negative externalities. The advocates, on the contrary, argue that access to a large retail market not only delivers direct economic benefits, but also a variety of positive spill‐over effects, and therefore, can be considered a consumer amenity that increases the attractiveness of the entry location. To test

Regional income convergence and divergence has been an active field of research for more than 20 years, and research papers in this field are still being produced at a prodigious rate. Despite their importance for the study of dynamics of income distribution, interactive visualization tools revealing spatiotemporal dimensions of the income data have been sparsely developed. This study introduces a

Residential satisfaction depends on housing and neighborhood conditions in addition to housing cost affordability. To determine the relative importance of these factors, their average effect is usually estimated using sample data, eventually split in subsamples to represent different levels of socioeconomic status. A concern about the division of households into groups is that, as groups are modified

Classification of similar travel behavior is essential for market segmentation research in geography and transportation science. Cluster analysis using sequence alignment measurement incorporates the sequential information embedded in activity‐travel sequences. The resultant clusters are then typically associated with the relevant variables. However, although the sequences are clustered by similar

In the presented research, the main aim is the assessment of machine learning (ML) techniques usage in the process of acquiring and formalizing generalization rules and variables, understood as settlement features, in settlement generalization. The research specifically addresses the problem of automated settlement selection for 1:1,000,000 scale. We focus on two processes of cartographic knowledge

The novel SARS‐CoV2 has disrupted health systems and the economy, and public health interventions to slow its spread have been costly. How and when to ease restrictions to movement hinges in part on whether SARS‐CoV2 will display seasonality due to variations in temperature, humidity, and hours of sunshine. Here, we address this question by means of a spatio‐temporal analysis in Spain of the incidence

Patterns of change in neighborhoods can be discordantly different, even within the same city district. A little understood factor in how urban neighborhoods form and grow is structural inertia, which is the tendency of an urban area to resist change due to its existing physical and socio‐economic fabric. This study explores how patterns of buildings, plots, blocks, and streets affect change or inertia

This study examines the relative effects of distance‐dependent versus localized amenities in changing urban spatial structure of residential location values within urban areas. We also investigate the spatio‐temporal patterns of neighborhood value, which is the sum of distance‐dependent and localized amenity effects, at a local community scale. We use a hedonic housing price model that controls for

Tobler’s first law of geography is widely recognized as reflecting broad empirical realities in geography. Its key concepts of “near” and “related” are intuitive in a univariate setting. However, when moving to the joint consideration of spatial patterns among multiple variables, the combination of attribute similarity and geographical similarity that underlies the concept of spatial autocorrelation

Simultaneous studies of multiple health conditions over geographic areas can be enhanced by the principal component analysis (PCA). However, the presence of spatial autocorrelation may induce nonlinearity that compromises PCA. This article presents an approach that combines the residual standardization method in PCA with a spatial regression method to account for spatial autocorrelation. It first estimates

The urban structure of large Chinese cities has been well researched, but a systematic analysis of polycentric urban development and the determinants of subcenter formation across municipal districts in cities at the prefectural level and above (PLACMD) is lacking. Using geospatial big data and spatial analysis methods, we measure the urban spatial structure of all 294 PLACMDs to determine the polycentric

In this article, we propose a new approach for assessing the extent to which schools are successful in mixing students from different backgrounds. It is based on a comparison of variation in the composition of the student population in small‐scale residential neighborhoods with variation in the composition of the student population at local schools. From this we compute a measure that corresponds to

This article examines the characteristics of residents moving into new rail transit neighborhoods using longitudinal, individual‐level data from the Housing Mortgage Disclosure Act. To disentangle the role of transit from other neighborhood amenities that may give rise to shifts in the socioeconomic or demographic profile of homebuyers, an exploratory text analysis is first performed on property advertisements

This article examines macro‐level contextual parameters and individual‐based factors deemed in the literature to directly influence individuals’ daily mobility practices. It considers the urban structure, place of residence, situation in the social hierarchy, and position in the life course. Taking its inspiration from approaches highlighting segregation at destination place and studies focusing on

In a 2020 Geographical Analysis paper, Pazitka, Wójcik, and Knight aimed to examine and challenge the validity of office‐location approaches for modeling intercity business flows. Their argument proceeded in three steps: (1) critique the office location approach and specifically the interlocking world city network model (IWCNM), (2) introduce their own inter‐organizational project approach (IOPA) as

In this commentary, I evaluate the potential of bipartite network projections of multilocational corporations in quantitative structural analyses of world/global cities. My starting point is that the Geographical Analysis paper by Pazitka, Wójcik, and Knight (2020) paper provides a number of useful critical observations in this regard, but their analysis is also incomplete and at points potentially

Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are

This article assesses the locally varying effects of gun ownership levels on total and gun homicide rates in the contiguous United States using cross‐sectional county data for the period 2009–2015. Employing a multiscale geographically weighted instrumental variables regression that takes into account spatial nonstationarity in the processes and the endogenous nature of gun ownership levels, estimates

Detection of changes in spatial processes has long been of interest to quantitative geographers seeking to test models, validate theories, and anticipate change. Given the current “data‐rich” environment of today, it may be time to reconsider the methodological approaches used for quantifying change in spatial processes. New tools emerging from computer vision research may hold particular potential

The interlocking world city network model and other office location approaches (OLAs) have become the most widely used empirical models of the world city network (WCN). Despite numerous methodological improvements, they continue to rely on a legacy of using data on office locations of firms to indirectly estimate intercity business flows. To advance the dialogue about how to improve on existing empirical

This article proposes a novel method for data‐driven identification of spatiotemporal homogeneous regions and their dynamics, enabling the exploration of their composition and extents. Using a simple network representation, the method enables temporal regionalization without the need for geographical harmonization. To allow for a transparent corroboration of our method, we use it as a basis for an

The subtitle of Geographical Analysis is “An International Journal of Theoretical Geography”, yet in fifty years of publication little has been published in the journal that fits that remit. In this short commentary I reflect on the nature of theory and explanation in geography and call for more sustained engagement with these themes in the pages of the journal in the years to come.

The Cox proportional hazard model is one of the most popular tools in analyzing time‐to‐event data in public health studies. When outcomes observed in clinical data from different regions yield a varying pattern correlated with location, it is often of great interest to investigate spatially varying effects of covariates. In this paper, we propose a geographically weighted Cox regression model for

Empirical applications of the Markov chain model and its spatial extensions suffer from issues induced by the sparse transition probability matrix, which usually results from adopting maximum likelihood estimators (MLEs). Two discrete kernel estimators with cross‐validated parameters are proposed for reducing the sparsity in the estimated transition probability matrix. Monte Carlo experiments suggest