Geographical Analysis最新文献

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 units to capture income variability within a neighborhood. To address this gap, we use Iterative Proportional Fitting (IPF) to combine neighborhood-level census data with an individual-level income survey data and then estimate small area discrete and continuous income distributions for each small area. We show that it is possible to compute segregation indices based solely on estimated probability distributions without the need to generate a full synthetic population or to obtain integer population counts. We test our empirical method with the case of Mexican cities, for which global and local indexes of segregation are computed with bootstrapped confidence intervals. The major contributions of this article are twofold. First, it uses a method for income-data generation to measure income segregation. Secondly, it demonstrates a linkage between the computation of segregation measures based on probability distributions and the feasibility of computing them directly from the same IPF estimated distributions of income.

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 an emphasis on its extension to incorporate multiple facilities. The only formulated multiplant Weber problem involving resource allocation remains unsolved due to its complexity. An effective approach integrating GIS processing (i.e., the Voronoi diagram and vector-based overlay) with the classic optimization algorithm (i.e., the Weiszfeld algorithm) is developed to address raw material sourcing in the process of siting facilities. The implementation relies entirely on open-source Python packages, making the work reproducible, replicable, and expandable. Application findings demonstrate that the utility and computational efficiency of the proposed method to tackle this challenging problem are superior to those of the most advanced commercial optimization software.

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 of finding spatial clusters when a range of spatial scales is examined, and when the multiple testing that this entails is accounted for. If there is uncertainty regarding the scale of the process, there is little cost in examining a range that includes spatial scales that are larger than the true cluster. Gains in the power to detect significant clustering may be had if the examination of cluster sizes that are clearly too small may be omitted. A small number of Bonferroni-adjusted tests will often provide a slight decline in statistical power, relative to tests that search comprehensively over a range, but may have the benefit of providing a relatively better estimate of the location of change.

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.

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 respect to their functional importance within the urban activity space. In the literature, such an importance concept is usually specified via the frequency function of trip flows. Considering the case that the less frequently visited place in reality may still be an important origin or an important destination, we propose a different method for the ranking of spatial units by introducing the structural property of trip network. The proposed method is inspired from the mutual reinforcing relationship between the trip origins and destinations: important destinations attract travel flows from important origins and at the same time important origins have many flows toward important destinations. Our experimental results show that the proposed method is effective in uncovering spatial interaction patterns of urban activities.

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 postretirement. His service and mentoring in many forms too were invaluable. In what follows, an overview of his career and contributions are provided. Additionally, observations and broader significance are offered as a set of rules to live by based on my three decades of interaction with Professor Getis.

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, the rectangular, and the noncartesian cases. We propose numerical simplifications to compute the impacts, avoiding the inversion of a large filter matrix. These simplifications considerably reduce computation time; they can also be useful for prediction. Furthermore, we define local measures for the intra, origin, destination and network effects. Interestingly, these local measures can be aggregated at different levels of analysis. Finally, we illustrate our methodology in a case study using remittance flows all over the world.

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 factors. To address this gap, we aim to identify and characterize the different trajectories of depopulation in Spain from 2000 to 2020 at the small area level using sequence analysis, spatial autocorrelation analysis, decomposition techniques, and multinomial logistic modeling. We show that while Spain recorded an overall 17.2% national population growth between 2000 and 2020, 63% of municipalities experienced depopulation. We identify six trajectories of population decline, with a well-defined northwest-south divide. These trajectories include mostly rural municipalities, but also certain small- and medium-sized cities. Natural decline comprises the main demographic component underpinning differences in the extent of depopulation across trajectories, and international migration plays an important role in explaining transitions to decline since the financial crisis of 2008. Small and old populations, and, to a lesser extent, remoteness from cities are key features characterizing areas of high decline.

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 included in the models consist of land use types, elements of the transport system and sociodemographic characteristics. Out of several regression models with different underlying distributions, a Tweedie generalized additive model is chosen by its values for AIC, RMSE, and sMAPE to be compared to an XGBoost model. To consider spatial relationships, spatial splines are included in the Tweedie model, while the estimations of the XGBoost model are modified using a geographically weighted regression. Both methods entail certain advantages: while XGBoost leads to far better values regarding RMSE and sMAPE and therefore to a better model fit, the Tweedie model allows an easier interpretation of the influence of the independent variables including spatial effects.