Geographical Analysis最新文献

Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's I (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity-based (considering first/higher-order and common border) and Euclidean distance-based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long-tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow-related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.

The lack of comprehensive spatial data for neighbourhoods in cities in the global South has posed a significant challenge for examining socio-economic inequities in accessibility to services. By combining the primary (survey data) and secondary data sources with new spatial data sources (Earth observation data, Google Maps), we create a spatial database of 4,145 residential locations in Delhi, aggregating them into 1 km grid-shaped neighbourhoods. The neighbourhood's economic status is evaluated using a composite index of the built environment, land price, and household income. Social characteristics are examined through the percentage of the scheduled caste (SC) population, considering their historical marginalization in Indian society. Using the E-2SFCA method, we calculate accessibility to four key services and employ the geographically weighted regression (GWR) model to explore inequities in accessibility based on neighbourhood location and socio-economic characteristics. Findings reveal inequity in accessibility to services at the neighbourhood level is primarily driven by spatial location rather than income or percentage of SC population. Moreover, the influence of socio-economic characteristics on accessibility varies across locations. The spatial data mapping approach employed in this article can be applied to numerous rapidly urbanizing cities in the global South lacking block or neighbourhood-level spatial data.

This study introduces the Access Weight Matrix (AWM) to capture the spatial dependence of access across a geographical surface. AWM is a nonsymmetry, nonzero diagonal matrix with elements to be a function of (i) the spatial distribution of places, (ii) the number of places, and (iii) the travel-time threshold to reach places rather than distance, contiguity, or adjacency. AWM is tested and validated to examine the spatial dependence of transit access to employment opportunities in the City of Chicago. Three observations are noticed. First, the degree of spatial dependence between the access of geographical units is not necessarily proportional to their proximity and is better explained by AWM than traditional spatial weight matrices regardless of the travel-time threshold. Second, the time-dependence feature of AWM improves the accuracy of capturing spatial dependence, particularly in short travel-time thresholds. Third, near geographical units are not necessarily more related than distant geographical units even for access that is proved to be spatially highly correlated with neighboring units. With the increased ease of measuring access, research is expanding to explore the socioeconomic, demographic, and built-environment correlates of access. AWM can be employed in developing more accurate spatial econometrics models.

Although neighborhoods are a widely used analytical concept in urban geography, they are often proxied using grids or statistical sectors in empirical research. The rationales underlying these proxies are often separated from the theoretical considerations of what makes a neighborhood a neighborhood, casting shadows over their relevance and applicability. In this article, we identify two specific challenges separating empirical operationalizations from theoretical considerations in neighborhood delineations: (1) not incorporating key built environment elements and (2) monodimensional approaches. We develop a method that addresses this double challenge by (1) creating morphological basic spatial units (BSUs) and (2) aggregating them into neighborhoods using multilayer community detection (MLCD) drawing on datasets used in both formal and functional regionalization approaches. We illustrate this method for the case of Leuven, Belgium, by (1) using street blocks as BSUs and (2) focusing on proximity, land use, and social interactions. Through a comparative analysis, we show that our results align with theoretical considerations and perform as well as, and perhaps better, than statistical sectors and grids as neighborhood representations. We therefore argue that this flexible method can bridge formal and functional regionalization approaches making the case for its adoption in neighborhood delineation exercises.

This study proposes a method for aggregating/synthesizing global and local sub-models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub-model, while the generalized product-of-experts method was used to aggregate these sub-models. The major advantages of the proposed method are as follows: (i) it is highly scalable for large samples in terms of accuracy and computational efficiency; (ii) it is easily implemented by estimating sub-models independently first and aggregating/averaging them thereafter; and (iii) likelihood-based inference is available because the marginal likelihood is available in closed-form. The accuracy and computational efficiency of the proposed method are confirmed using Monte Carlo simulation experiments. This method was then applied to residential land price analysis in Japan. The results demonstrate the usefulness of this method for improving the interpretability of spatially varying coefficients. The proposed method is implemented in an R package spmoran.

The p-dispersion problem is a spatial optimization problem that aims to maximize the minimum separation distance among all assigned nodes. This problem is characterized by an innate spatial structure based on distance attributes. This research proposes a novel approach, named the distance-based spatially informed property (D-SIP) method to reduce the problem size of the p-dispersion instances, facilitating a more efficient solution while maintaining optimality in nearly all cases. The D-SIP is derived from investigating the underlying spatial characteristics from the behaviors of the p-dispersion problem in determining the optimal location of nodes. To define the D-SIP, this research applies Ripley's K-function to the different types of point patterns, given that the optimal solutions of the p-dispersion problem are strongly associated with the spatial proximity among points discovered by Ripley's K-function. The results demonstrate that the D-SIP identifies collective dominances of optimal solutions, leading to building the spatially informed p-dispersion model. The simulation-based experiments show that the proposed method significantly diminishes the size of problems, improves computational performance, and secures optimal solutions for 99.9% of instances (999 out of 1,000 instances) under diverse conditions.

We propose and illustrate a general framework in which spatial autocorrelation is measured by the Frobenius product of two kernels, a feature kernel and a spatial kernel. The resulting autocorrelation index $��\delta �$ generalizes Moran's index in the weighted, multivariate setting, where regions, differing in importance, are characterized by multivariate features. Spatial kernels can traditionally be obtained from a matrix of spatial weights, or directly from geographical distances. In the former case, the Markov transition matrix defined by row-normalized spatial weights must be made compatible with the regional weights, as well as reversible. Equivalently, space is specified by a symmetric exchange matrix containing the joint probabilities to select a pair of regions. Four original weight-compatible constructions, based upon the binary adjacency matrix, are presented and analyzed. Weighted multidimensional scaling on kernels yields a low-dimensional visualization of both the feature and the spatial configurations. The expected values of the first four moments of $��\delta �$ under the null hypothesis of absence of spatial autocorrelation can be exactly computed under a new approach, invariant orthogonal integration, thus permitting to test the significance of $��\delta �$ beyond the normal approximation, which only involves its expectation and expected variance. Various illustrations are provided, investigating the spatial autocorrelation of political and social features among French departments.

Total demand suitably served and facility workload balance are two important considerations in location coverage. Previous work has dealt with workload balancing issues using a number of approaches, including imposing facility capacities and the use of multiple objectives focused on workload variation. However, a facility is usually restricted to a single service unit, inconsistent with strategies that allow for increased staffing such as multiple service units in dealing with higher levels of demand. This article proposes a new bi-objective optimization model that maximizes total demand coverage and minimizes workload differences simultaneously while allowing more than one service unit to be co-located at a site. Since the proposed model is strongly NP hard, a heuristic algorithm is developed for efficient solution. The model is applied to support postal delivery service planning. Results show that the proposed model offers improved performance compared to approaches that do not permit co-location. The proposed algorithm is able to produce high-quality solutions that evenly distribute allocated service demand, and does so much faster with higher-quality solutions compared to exact solution approaches.

Privacy and utility are two important objectives to consider when releasing census data. However, these two objectives are often conflicting, as protecting privacy usually necessitates introducing noise into the data, which compromises data utility. Determining the appropriate level of privacy protection presents a significant challenge in the data release. Therefore, it is necessary to investigate the tradeoff between privacy and utility before making a final decision on the level of privacy protection. In this article, we propose a multiobjective optimization framework to generate multiple optimal solutions that satisfy the two objectives of privacy and utility, as well as to analyze the tradeoff between privacy and utility for decision-making. This framework relocates individuals susceptible to revealing their identities to protect their privacy. We maximize the number of individuals relocated while maximizing the utility of the data after relocations. The proposed framework is tested using synthetic population data in Franklin County, Ohio. Our experimental results show that the framework can efficiently generate a collection of optimal solutions and can be used to effectively balance privacy and utility.