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Everyday mobility is critical to understand migrants' accessibility and inclusions to the destination cities. Existing research was insufficient to explain the everyday movement of relatively privileged skilled migrants from the Global North to the Global South, nor did it distinguish the role of one's capability and intentionality in shaping mobility. To fill in the research gap, we employ the capital-mobility framework to investigate how skilled U.S. migrants' capital accumulation and intentionality jointly affect their everyday mobility in Chinese cities, and the implications for their social inclusion in the local society. We use sketch mapping with semistructured interviews to conduct this study in the Pearl River Delta region, a pioneer of economic development in China. We find that although migrants' capital accumulation facilitated their everyday movement, COVID-19 disrupted such mobility. Whether migrants had intentions to move varied, shaped by geopolitical relations, urban environment, and migrants' own capital. The voluntary or involuntary nature of their everyday mobility also suggests whether their social inclusion or exclusion in the local society is by choice or constraint. Our study integrates geographic information systems visualization with qualitative data to highlight the spatial mobility and immobility at the individual level and generates policy implications for retaining global talent at the city level. [ABSTRACT FROM AUTHOR]

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei’s official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters. [ABSTRACT FROM AUTHOR]

Geoforensic science investigates the location and time of criminal occurrences by integrating multiple fields, including geography, criminology, ecology, biology, and geology. The ubiquity, durability, and spatial-temporal predictability make pollen a frequently used biomarker in geoforensic investigations to help determine the provenance of hard-to-trace items, including computers, counterfeit products, digging equipment, clothing, and undetonated explosives. The recently developed Geoforensic Interdiction (GOFIND) model links the pollen combination collected from a sample object with the probability of locations traversed by the object. Although the GOFIND model improves over the traditional single-site joint probability approach and can be used to identify multiple locations simultaneously, substantial limitations remain. In particular, GOFIND requires specifying the number of locations traversed by an object in advance—a priori knowledge that is almost impossible to obtain in real-world applications. This article aims to introduce the GOFIND + model that leverages detected and undetected pollen to establish a probabilistic relation between pollen and the corresponding species distribution in the environment. Our simulation tests using the USDA CropScape data for the state of Texas show that the GOFIND + model outperforms the GOFIND model in predictive accuracy. Further, GOFIND + does not require that users specify the number of geographical stops and sites a priori. Key Words: geoforensics, GOFIND+, pollen, spatial optimization. [ABSTRACT FROM AUTHOR]

Distance is one of the most important concepts in geography and spatial analysis. Since distance calculation is straightforward for points, measuring distances for non-point objects often involves abstracting them into their representative points. For example, a polygon is often abstracted into its centroid, and the distance from/to the polygon is then measured using the centroid. Despite the wide use of representative points to measure distances of non-point objects, a recent study has shown that such a practice might be problematic and lead to biased coefficient estimates in regression analysis. The study proposed a new polygon-to-point distance metric, along with two computation algorithms. However, the efficiency of these distance calculation algorithms is low. This research provides three new methods, including the random point-based method, polygon partitioning method, and axis-aligned minimum areal bounding box-based (MABB-based) method, to compute the new distance metric. Tests are provided to compare the accuracy and computational efficiency of the new algorithms. The test results show that each of the three new methods has its advantages: the random point-based method is easy to implement, the polygon partitioning method is most accurate, and the MABB-based method is computationally efficient. [ABSTRACT FROM AUTHOR]

Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process. [ABSTRACT FROM AUTHOR]

Choropleth mapping provides a powerful way to visualize geographical phenomena with colors, shadings, or patterns. In many real-world applications, geographical data often contain uncertainty. How to incorporate such uncertainty into choropleth mapping is challenging. Although a few existing methods attempt to address the uncertainty issue in choropleth mapping, there are limitations to widely applying these methods due to their strong assumption on the distribution of uncertainty and the way in which similarity or dissimilarity is assessed. This article provides a new classification scheme for choropleth maps when data contain uncertainty. Considering that in a choropleth map, units in the same class are assigned with the same color or pattern, this new approach assumes the existence of a representative value for each class. A maximum likelihood estimation–based approach is developed to determine class breaks so that the overall within-class deviation is minimized while considering uncertainty. Different methods—including linear programming, dynamic programming, and an interchange heuristic—are developed to solve the new classification problem. The proposed mapping approach has been applied to map the median household income data from the American Community Survey and simulated disease occurrence data. Test results show the effectiveness of the new approach. The linkage between the new approach and the existing methods is also discussed. Key Words: choropleth mapping, map classification, maximum likelihood estimation, uncertainty. [ABSTRACT FROM AUTHOR]

Adequate access to healthy food has become a social issue due to the recent Great Recession and heightened levels of unemployment. Geographers have focused their attention on how to accurately evaluate food access and how to identify and delineate food deserts; that is, low-income neighborhoods where affordable and healthy food is lacking or limited. Findings of recent food access studies are, however, dramatically inconsistent. We argue that spatial scale and the level of aggregation used in constructing food access measures could account for a major portion of the varying results. We draw on an empirical study in the Tucson, Arizona, metropolitan area, to examine how varying geographic scales and aggregation methods affect food access assessment. We also provide an analysis to show how spatial scale and aggregation practices lead to inconsistent conclusions about food access and designation of food deserts. [ABSTRACT FROM AUTHOR]

Precipitation stations are important components of a hydrological monitoring network. Given their critical role in rainfall forecasting and flood warnings, along with limited observation resources, determining the optimal locations to deploy precipitation stations presents an important problem. In this paper, we use a maximal covering location problem to identify the best precipitation station sites. Considering the terrain conditions and the characteristics of a rainfall network, the original maximal covering location model is modified with the introduction of a set of additional constraints. The minimum density requirement is used to determine a precipitation station’s coverage range, and three weighting schemes are used to evaluate each demand object’s covering priority. As a typical mountainous watershed with high annual precipitation, the Jinsha River Basin is selected as the study area to test the applicability of the proposed method. Results show that the proposed method is effective for precipitation station configuration optimization, and the model solution achieves higher coverage than the real-world deployment. Compared with the commercial solver CPLEX, a genetic algorithm-based heuristic can significantly reduce the computation time when the problem size is large. Several deployment strategies are also discussed for establishing the optimal configuration of precipitation stations. [ABSTRACT FROM AUTHOR]

ABSTRACT Demand for service in location modelling is often evaluated based on the spatial proximity of fixed and static reference locations of demand (e.g. home) to a facility, which ignores person-specific activity-travel patterns and the temporal changes in demand for service throughout the day. To address these limitations, this study draws upon recent developments in space-time measures of individual accessibility to explore the spatial and temporal structures of demand by considering individuals' space-time constraints and impact of existing urban structures. Based on a time-geographic framework, eight space-time demand measures were developed and compared with three conventional location-based demand measures for 12 hospitals through an empirical study conducted in Columbus, Ohio. The results show that geographic proximity between clients' home and facilities may not be an effective indicator for service demand, and conventional demand measures tend to underestimate potential demand for service in most situations. The study concludes that space-time demand measures that take into account people's activity-travel patterns in space-time would lead to better estimation of demand for service in most cases. [ABSTRACT FROM AUTHOR]

Covering models have been used in a wide range of modeling and geospatial analysis applications ranging from planning emergency services to natural reserve design. One topic in coverage modeling that has received considerable research attention is addressing uncertainty due to facility unavailability and service disruptions. In this article, we propose a covering model that maximizes the expected coverage of demand by considering the possibility of facility failures. Unlike existing models that assume a uniform failure probability across all sites in an area, the proposed model can account for spatially varying failure probabilities and describes better the underlying geographic processes that cause facility failures. The model is posed as a spatial optimization problem using integer linear programming. We compare two different formulations of the covering model and discuss their properties. The proposed model formulations have been tested computationally using a warning sirens data set that has been widely used in assessing covering models. We conclude with a summary of findings as well as possible directions of future research. [ABSTRACT FROM PUBLISHER]

This article discusses spatial optimization in geography, focusing on contributions of geographers in explicit geographical contexts. An overview of spatial optimization is given, as well as illustrative examples. Many of the individuals contributing to this area of the discipline are identified, demonstrating the breadth of academic institutions spanning the globe where spatial optimization is represented in the research and curriculum of geographers. The article provides a characterization of what a spatial optimization problem is, but also properties, relationships, and challenges behind this. The ultimate purpose of this article is to highlight the spatial optimization subspecialty within geography and in doing so, highlight the need for continued spatial model development and application in the discipline. Further, there is also a need for research focused on techniques to solve spatial optimization problems, particularly in the context of geographic information systems. [ABSTRACT FROM PUBLISHER]

Uncertainties and errors associated with aggregation have long been recognized in the study of spatial problems. In facility location modeling, while much has been done to examine the aggregation of large datasets of discrete points, errors and uncertainties involved in aggregating continuous spatial units are not well understood. This study focuses on the effects of aggregating continuous spatial units into discrete points within the context of the location set covering problem. We propose new measures to understand and quantify errors associated with a continuous aggregation scheme. In a real-world application, the proposed methods can be used to suggest an appropriate aggregation scheme before the application of the location model. We demonstrate the concepts developed here with an empirical study of siting emergency warning sirens in the city of Dublin, OH. [ABSTRACT FROM PUBLISHER]

Many government agencies and corporations face locational decisions, such as where to locate fire stations, postal facilities, nature reserves, computer centers, bank branches, and so on. To reach such location-related decisions, geographical information systems (GIS) are essential for providing access to spatial data and analysis tools. Moreover, geographic insights can be gained from GIS as they enable capabilities for better reflecting problems of interest in location modeling. The resulting models can be complex, however, and hence computationally challenging to solve. This article examines an important model for regional service coverage maximization. This model is solved heuristically using a genetic algorithm. The new heuristic innovatively incorporates problem-specific knowledge by exploring the geographical structure of the problem under study. Comparative application results demonstrate important nuances of the new genetic algorithm, enhancing overall performance. [ABSTRACT FROM AUTHOR]

Facility placement and associated service coverage are major concerns in urban and regional planning. In this paper an approach is detailed for the problem of covering spatial demand for service, where potential facilities are located in the continuous plane. It is shown that weighted demand, represented as points, lines or polygons, can be optimally served by a finite number of potential facility locations, called the polygon intersection point set (PIPS). The developed approach is an extension of a point-based abstraction of demand to more general representations (e.g. points, lines or polygons). An empirical analysis of warning siren siting in Ohio is carried out, highlighting the applicability of this approach. [ABSTRACT FROM AUTHOR]