Geographical Analysis最新文献

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 challenging. This article, therefore, considers the relative variability in streetscape skeletons and their spatial associations and answers the following question: where are harmonious or inharmonious streetscape skeletons locally clustered? The relative variability in the streetscape skeletons in the districts of the Tokyo metropolitan region was computed, where building heights and setbacks are directly controlled through streetscape promotion district planning stipulated in the City Planning Act. The spatial association of the relative variability in streetscape skeletons was then visualized to identify where streetscape skeletons are harmonious and inharmonious. The results showed that although harmonious streetscape skeletons are spatially clustered inside the domain bounded by wider streets, such clusters are small and spatially scattered. We, therefore, concluded that the effectiveness of direct controls through streetscape-promoting district planning to maintain and create harmonious streetscape skeletons is spatially limited and thus insufficient.

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 changes in spatial accessibility to restaurants, identified hot spots of communities with large accessibility reductions, and explored relationships between the reductions and neighborhood characteristics in Leon County, Florida. Analysis showed that the area experiencing the largest reductions in spatial access to restaurants varied by transportation modes and the overall geographic patterns in accessibility reductions significantly differed. Communities with significant reductions in car-based accessibility were areas with a small percentage of the older and non-White populations and a longer distance to the central area. On the other hand, only being a shorter distance to the central area was more related to hot spots of changes in walking accessibility. Findings show geographic patterns of restaurant closures, which interacted with people's modes of transportation. As such, the closures most substantially affected people in more suburban areas who might rely on driving during the pandemic.

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 affect local neighborhoods. To explore these questions, this research uses spatial hedonic methods to estimate the implicit value of accessibility to schools in the City of Hamilton, Ontario. Spatial Durbin model results provide evidence of a significant negative correlation between distance to schools and housing prices in the Canadian context. This suggests that accessibility to schools is capitalized into property values and that the closure of a neighborhood school may result in potentially significant losses of economic value in communities.

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 eigenvector spatially filtering-based spatially varying coefficient (RE-ESF-SVC) model and the generalized lasso (GL) technique. Additionally, a restricted maximum likelihood estimation (REML)-based two-step iterative algorithm is developed for parameter estimation. Simulation experiments and an empirical application using rental price data confirm the ability of the proposed model to identify multiscale continuous and discrete spatial heterogeneity.

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 unclear what underlying perspectives are guiding decisions to concentrate on certain aspects of urban inequalities, while potentially ignoring others. We address this gap by reviewing the literature centered on the geospatial analysis of urban inequalities and identify three predominant research lenses from accessibility, distribution, and policy and stakeholder perspectives. As a primary contribution of this article, we connect the perspectives with ideas drawn from complexity theory to develop an overarching socio-technical framework for how urban inequalities emerge over space and time. While traditional scientific frameworks seek to increase knowledge through causality, complexity science acknowledges the inherent challenges in defining, understanding and solving complex problems such as urban inequalities, which has profound implications for their representation, modeling and interpretation. We critically reflect on the framework through key relational themes and insights drawn from the literature and close with considerations for future research.

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.

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.

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.

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 between different event patterns and visualizes its spatial variation. The methods help us understand events' properties, consider their underlying mechanisms, and permit us to prevent events if they represent undesirable phenomena such as crimes and traffic accidents. The proposed methods are applied to analyze the population distribution in the central area of Tokyo in May 2019. The application revealed the spatial variation of population peaks in this area and the differences in population patterns between different types of days.

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.