Geographical Analysis最新文献

Drawing on data from the Eurobarometer Survey, this study explores the distribution of stigma and discrimination towards LGBTI communities at the sub-national level. There has been increased attention at global and pan-European levels around LGBTI rights mostly drawing on national-level measurements. However, there is limited research or understanding of the complex and pervasive problem of stigma and discrimination towards LGBTI groups at regional levels. Yet, it is widely noted that regional disparities exist across demographic characteristics; thus, national-level data may not be suitable for planning and policy making. We utilized two questions from the Eurobarometer as a proxy for levels of stigma and discrimination against LGBTI communities. We drew on novel Small Area Estimation (SAE) methods to produce the first reliable estimates and analysis for sub-national areas across Europe. The findings widen our understanding of differences around stigma and discrimination towards LGBTI communities both between and within nation states, emphasizing how regional-level analysis is necessary to develop targeted policies and interventions. Our findings demonstrate that programming and policy based on only national data should be utilized with caution. We argue that novel methods, such as SAE, can be utilized to support more effective data-driven decision making.

This article introduces a time-series clustering approach for classifying, visualizing, and exploring neighborhood dynamics. We illustrate the method for the case of racial and ethnic dynamics of neighborhoods in 64 U.S. metropolitan areas from 1990 to 2020. We establish typologies of continuous attribute trajectories for the share of Black, White, and Hispanic populations at the census tract level and explore generalizability versus specificity tradeoffs when varying the cluster analysis scale. Our results affirm a consistent decline in White population shares in neighborhoods across most metropolitan areas, accompanied by varied increases in Black and Hispanic populations. We also highlight the importance of metropolitan context in shaping neighborhood trends. While all cities show a trend towards increased diversity, the specific patterns and rates of change vary considerably, highlighting the unique demographic dynamics at play in each metropolitan area. The time-series clustering approach offers some advantages over previously used methods for visualizing and classifying longitudinal neighborhood dynamics like sequence analysis or growth change modeling in that it clusters the full continuous time series and does assume a pre-determined functional form.

OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design—in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.

Spatially and temporally varying coefficient (STVC) models are attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients. However, these models often struggle with balancing the computational efficiency, flexibility, and interpretability of the coefficients. This study develops a fast and flexible STVC model to address this challenge. To enhance flexibility and interpretability, we assume multiple processes in each varying coefficient, including purely spatial, purely temporal, and spatio-temporal interaction processes with or without time cyclicity. We combine a pre-conditioning method with a model selection procedure, inspired by reluctant interaction modeling, to estimate the strength of each process in each coefficient in a computationally efficient manner, while removing redundant processes as necessary. Monte Carlo experiments demonstrate that the proposed method outperforms alternatives in terms of coefficient estimation accuracy and computational efficiency. We then apply the proposed method to a crime analysis. The result confirms that the proposed method provides reasonable estimates. The STVC model is implemented in the R package spmoran.

The analytical hierarchy process (AHP) is a widely used approach and a decision rule to derive criteria weights in geographic information system-based multi-criteria evaluation (GIS-MCE). However, one limitation of the AHP method is that it constrains the number of criteria that can be meaningfully weighted to typically seven to nine criteria. Recently, machine learning (ML) techniques have emerged as a compelling alternative for deriving criteria weights. This research aims to assess the capabilities of ML-MCE in handling a larger number of criteria and is specifically applied to a case study of urban suitability analysis. The random forest (RF) ML technique is used to evaluate the ability of the MCE method to handle up to 27 criteria. Geospatial data from the Metro Vancouver Region, Canada, are used, with the criteria subdivided into 11 groups starting with the most basic seven criteria and incrementally adding two new criteria per group. The results indicate the RF-ML approach can manage a larger number of criteria compared to the traditional AHP approach, with 15 criteria providing a meaningful upper threshold, demonstrating its potential to accommodate a wider range of stakeholder preferences for complex urban suitability analysis contexts.

The subject of inequality and its geographical dimensions has seen a surge of interest in recent years. However, existing work tends to study inequality through single spatial scales, even though processes driving inequality operate at and across multiple scales. This article, therefore, investigates how inequality at the regional and local scale relates to phases of economic development in Sweden over three decades. The findings point towards a diverging trend of inequality between the regional and local scale, with a noticeable shift at the turn of the millennium. While the last decades were characterized by a slight regional convergence, inequality at the local scale continued to increase. Accounting for different regional contexts, economic growth and local inequality were most pronounced in the larger urban areas. Surprisingly, though, in the last decade, employment grew in urban areas without an increase in local inequality. In contrast, peripheral and sparsely populated regions experienced a rise in inequality without significant employment growth. This suggests that the link between economic development and inequality is not universal but dependent on, among others, the nature of structural change in the economy and institutional preconditions.

Traditional spatial interaction (SI) models of migration are susceptible to misspecification when the spatial structure of locations is not properly incorporated. To address this, a novel SI model for migration is introduced that integrates the theories of competing destinations (CD) and intervening opportunities (IO) to capture multiscale spatial structure using the recent generalized additive spatial smoothing (GASS) framework. This GASS CDIO model can identify the appropriate spatial scales to represent the spatial structure of origins and destinations in a data-driven manner. Validation of the model was conducted through two simulation experiments. The first demonstrates that employing the incorrect scale to capture spatial structure in SI models biases the parameter estimates and increases uncertainty. The second demonstrates that the GASS approach reliably recovers accurate parameters by identifying optimal hyperparameters associated with multiple spatial scales. The GASS CDIO model was then applied to U.S. inter-county migration data and compared to several other model specifications. The results reveal the unique spatial structure from the perspective of origins and destinations and illustrate the improved recoverability of anticipated migration relationships. This work suggests that the GASS CDIO model better integrates spatial theories of migration and accounts for the multiscale nature of SI processes.