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

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

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

The scientific method is predicated on the assumption that research designs and results can be reproduced and replicated. However, recent findings in some disciplines suggest that many studies fail to reach this standard, moving issues surrounding reproducibility and replicability forward into the research agenda of those fields. While the topic has yet to become a point of controversy in geography

Accessibility, measuring the ease of reaching potential destinations, is increasingly being considered as an effective indicator to evaluate the performance of transport and land use interactions. Primal accessibility, a generalization of the first accessibility formulation proposed by Hansen, has been widely used in many studies and demonstrated to be a reliable tool for project, program, and policy

A range of data is of geographic interest but is not available at a small area level from existing data sources. Small area estimation (SAE) offers techniques to estimate population parameters of target variables to detailed scales based on relationships between those target variables and relevant auxiliary variables. The resulting indirect small area estimate can deliver a lower mean squared error

The present paper examines the importance of integrating geographical effects into the analysis of social networks. Specifically, we study the impacts of spatial distance and territorial borders on information exchange within two European cross‐border regions where there is evidence of extensive cross‐border political interaction in the domain of public transportation. We use exponential random graph

The article analyzes area‐wide alcohol‐related driving crash rates, with an emphasis on neighborhood effects, edge effects, and spatial effects arising from shared roadways that traverse area boundaries. Using township data for the state of Indiana, spatial Durbin models of alcohol‐related driving crash rates are presented. The results suggest that a township's population composition and its abundance

The literature is replete with acknowledgments that spatial autocorrelation (SA) inflates the variance of a random variable (RV), and that it also may alter other RV distributional properties. In most studies, impacts of SA have been examined only for the three most commonly used distributions: the normal, Poisson (and its negative binomial counterpart), and binomial distributions; much less is known

We discuss a simple methodology to enable a statistical comparison of human population with the vegetation of North America over the past 13,000 years. Nonparametric kernel methods are applied for temporal and spatial smoothing of point data obtained from the Neotoma Paleoecology Database and the Canadian Archaeological Radiocarbon Database, which results in sequences of maps showing the development

This study examines issues of Small Area Estimation that are raised by reliance on the American Community Survey (ACS), which reports tract‐level data based on much smaller samples than the decennial census long‐form that it replaced. We demonstrate the problem using a 100% transcription of microdata from the 1940 census. By drawing many samples from two major cities, we confirm a known pattern: random

This article proposes a continuous approximation model for determining the number of hierarchical facilities when lower level facilities are subject to failures. The average distance from customers to the nearest open facility is derived for two types of customer behavior. The optimal number of facilities that minimizes the average distance is then obtained. The analytical expression for the optimal

To accommodate network allocation, population polygons are frequently transformed into singular, weighted centroids which are then integrated into the network either by snapping each centroid to the nearest network segment or by generating an artificial connector that becomes part of the network. In this article, an investigation of the connection method of network allocation is undertaken with two

This paper considers the past and future of the journal Geographical Analysis (GA), as well as the broader field of spatial analysis. From my experiences as a former editor of GA, I first identify three external trends that I feel will provide the backdrop for the future evolution of spatial analysis. These surround the rise of artificial intelligence, machine learning, and data science as disciplines

Movement is the driving force behind the form and function of many ecological and human systems. Identification and analysis of movement patterns that may relate to the behavior of individuals and their interactions is a fundamental first step in understanding these systems. With advances in IoT and the ubiquity of smart connected sensors to collect movement and contextual data, we now have access

The fiftieth anniversary of Geographical Analysis occurs for many quantitative human geographers at an exciting era of new and exciting sources of data and computational advancements. However, it also represents a crossroads where excitement to quickly capitalize upon these new data sources and methods risks reverting the discipline back to its abstraction‐rich and overly generalized roots, erasing

This paper investigates the effect of daily wind direction and speed on the spatio‐temporal distribution of particulate matter, . Interdependencies between the values of different monitoring sites are characterized by incorporating time‐varying anisotropic spatial weighting matrices. These weights are parameterized with respect to wind direction, speed and a range that marks the bandwidth of admissible

This article is concerned with the semantics associated with the statistical analysis of spatial data. It takes the simplest case of the prediction of variable y as a function of covariate(s) x, in which predicted y is always an approximation of y and only ever a function of x, thus, inheriting many of the spatial characteristics of x, and illustrates several core issues using “synthetic” remote sensing