Integrating the Huff Model and Floating Catchment Area Methods to Analyze Spatial Access to Healthcare Services

Analysis of spatial access to healthcare services is critical for effective health resource planning. Gravity‐based spatial access models have been widely used to estimate spatial access to healthcare services. Among them, the floating catchment area (FCA) methods have been proved to be informative and helpful to the designation of Health Professional Shortage Areas (HPSAs). This article integrates the Huff Model with the FCA method to articulate population selection on services. Through the proposed approach, population demand on healthcare services is adjusted by a Huff Model‐based selection probability that reflects the impacts of both distance impedance and service site capacity. The new approach moderates the over‐ or under‐estimating of population demand that occurred with previous methods. Furthermore, the method uses a continuous distance impedance weight function instead of the arbitrarily defined subzones of previous studies. A case study of spatial access to primary care in Springfield, MO, showed that the proposed method can effectively moderate the population demand on service sites and therefore can generate more reliable spatial access measures.     

陕西省抗震救灾地理信息辅助支持系统设计与实现

利用GIS技术和地理空间数据为抗震救灾提供空间服务,为决策者提供辅助决策信息是抗震减灾的重要技术手段。本文结合5．12大地震中陕西省抗震救灾地理信息辅助支持系统建设来介绍GIS在抗震救灾中的具体应用。     

Evaluation and development of sustainable urban land use plans through spatial optimization

Along with rapid global urbanization, cities are challenged by environmental risks and resource scarcity. Sustainable urban planning is central to address the dilemma of economic growth and ecosystem protection, where the use of land is critical. Sustainable land use patterns are spatially explicit in nature, and can be structured and addressed using spatial optimization integrating GIS and mathematical models. This research discusses prominent sustainability concerns in land use planning and suggests a generalized multi‐objective spatial optimization model to facilitate conventional planning. The model is structured to meet land use demand while satisfying the requirements of the physical environment, society and economy. Unlike existing work relying on raster data, due to its simple data structure and ease of spatial relationship evaluation, this research develops an approach for identifying land use solutions based on vector data that better reflects the actual shape and spatial layout of land parcels as well as the ways land use information is managed in practice. An evolutionary algorithm is developed to find the set of efficient (Pareto) solutions given the complexity of vector‐based representations of space. The proposed approach is applied in an empirical study of Dafeng, China in order to support local urban growth and development. The results demonstrate that spatial optimization can be a powerful tool for deriving effective and efficient land use planning strategies. A comparison to results using a raster data approach supports the superiority of land use optimization using vector data as part of planning practice.     

Optimal spatial data matching for conflation: A network flow‐based approach

Spatial data conflation involves the matching and merging of counterpart features in multiple datasets. It has applications in practical spatial analysis in a variety of fields. Conceptually, the feature‐matching problem can be viewed as an optimization problem of seeking a match plan that minimizes the total discrepancy between datasets. In this article, we propose a powerful yet efficient optimization model for feature matching based on the classic network flow problem in operations research. We begin with a review of the existing optimization‐based methods and point out limitations of current models. We then demonstrate how to utilize the structure of the network‐flow model to approach the feature‐matching problem, as well as the important factors for designing optimization‐based conflation models. The proposed model can be solved by general linear programming solvers or network flow solvers. Due to the network flow formulation we adopt, the proposed model can be solved in polynomial time. Computational experiments show that the proposed model significantly outperforms existing optimization‐based conflation models. We conclude with a summary of findings and point out directions of future research.     

改进G2SFCA的深圳市室内应急避难场所空间可达性研究

以深圳市为研究区,在考虑距离衰减效应的重力型两步移动搜索法（gravity two-step floating catchment area method,G2SFCA）基础上,引入Huff模型,并考虑需求点位置要素及地形要素,分别从供给点竞争、需求点定位、路网距离优化三方面对G2SFCA进行改进,并采用改进的G2SFCA对深圳市室内应急避难场所空间可达性进行研究。实验结果表明,改进的G2SFCA不仅能很好地揭示深圳市各社区室内应急避难场所的空间可达性,而且也能有效反映避难场所可达性与人口密度分布的空间匹配规律,较其他模型更符合现实情况;已建成室内应急避难场所数量、容纳人口及空间可达性的统计与分析结果真实、可靠,相关结论可为深圳市未来的防灾工作以及室内应急避难场所的空间规划与建设提供参考。     

Spatial microsimulation modeling for residential energy demand of England in an uncertain future

High quality infrastructure is crucial to economic success and the sustainability of society. Infrastructures for services, such as transport, energy, and water supply, also have long lead times, and therefore require effective long-term planning. In this paper, we report on work undertaken as part of the UK Infrastructure Transitions Research Consortium to construct long-term models of demographic change which can help to inform infrastructure planning for transport, energy, and water as well as IT and waste. A set of demographic microsimulation models (MSM), which are spatially disaggregate to the geography of UK Local Authorities, provides a high level of detail for understanding the drivers of changing patterns of demand. However, although robust forecasting models are required to support projections based on the notion of ‘predict-and-provide,’ the potential for behavioral adaptation is also an important consideration in this context. In this paper, we therefore establish a framework for linkage of a MSM of household composition, with behavior relating to the consumption of energy. We will investigate variations in household energy consumption within and between different household groups. An appropriate range of household types will be defined through the application of decision trees to consumption data from a detailed survey produced by the UK Department of Energy and Climate Change. From this, analysis conclusions will be drawn about the impact of changing demographics at both household and individual level, and about the potential effect of behavioral adjustments for different household groups.     

Mapping the spatial disparities in urban health care services using taxi trajectories data

The understanding of spatial inequality in health care services is critical for reasonable policy‐making and management. In this study, we present a novel approach to analyze the demand–supply of health care services using taxi data. Taxi data provide observations of individual travel activities, and hence can be used to characterize the actual demand–supply of health care services. We apply the proposed approach in Guangzhou, China to carry out a case study. The results show the spatial disparities in health care service access. About 21.05% of the total population has high hospital accessibility, while the remaining 78.95% has relatively low hospital accessibility (i.e., roughly an “80/20” distribution). It is found that 6.29% of the population lives in high‐density suburban communities but has relatively low hospital accessibility. Most of the hospitals serve a population that is compatible with their capacity. One hospital is found to have a small capacity but to serve a large population, while two hospitals have relatively high capacities but serve small populations. These findings can help improve our understanding of spatial inequalities in public service provision, and may also provide useful information to address the health care problems of an aging population in contemporary, rapidly urbanizing China.     

Spatial optimization for land acquisition problems: A review of models,solution methods,and GIS support

This article reviews the interdisciplinary research field of spatial optimization for land acquisition problems. We start with a theoretical framework to identify three categories of spatial optimization models: problems with aspatial constraints, location models, and problems with topological constraints. Exact, heuristic, and metaheuristic approaches to solving these problems are critically discussed. Tools that are available in commercial and open‐source GIS packages are reviewed from four aspects. We first survey the off‐the‐shelf support and then the development environments in these packages. A case study of the one‐center problem is used to illustrate the computational performance of different solution methods. Finally the advantages and disadvantages of current GIS data models are discussed. The article concludes with challenges and future directions for solving spatial optimization problems for land acquisition.     

The Role of Spatial Analysis in Livestock Research for Sustainable Development

The role of spatial analysis in livestock research for sustainable development is pervasive because its versatility supports livestock researchers at many stages of their work. Spatial analysis assists case study identification, characterizes the geographic context of research findings, and subsequently is used to assess the context for out-scaling purposes. Components of livestock systems and elements of their context and interrelationships can be characterized with an explicit spatial dimension. Output from spatial analysis linked to the results of simulation and optimization models provides support for decision-making. This is especially important where decisions involve different disciplines and interaction with a variety of experts usually working together in a sustainable development team.     

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

Traffic congestion represents an ongoing serious issue in many large cities. Many public facilities, such as hospitals, tend to be centrally located to ensure they are most accessible to local residents; as a result, they may contribute significantly to a city's traffic congestion. In this study, a multi‐objective spatial optimization model was provided to help formulate hospital relocation plans, taking into account both traffic congestion and hospital accessibility. Using intra‐urban movement data, we proposed a method to estimate the area‐wide traffic congestion caused by hospital visits and to identify potential hospitals to be relocated. An NSGA‐II (Non‐dominated Sorting Genetic Algorithm II) algorithm was applied to solve the hospital relocation optimization problem; we applied our model to study optimal hospital relocation plans in Beijing. Analysis results provide a tradeoff between traffic congestion relief and hospital accessibility. We discussed plans that significantly reduce traffic congestion while maintaining a high level of hospital accessibility. Our study has significant policy implications and provides insights for future facility planning and transportation planning.     

位置地图的空间认知研究

作为信息时代实现位置服务的重要基础,位置地图交互性强、使用方便,并且能实时、应景地向用户提供位置关联信息,因而比传统的电子地图更能满足用户的位置服务需求。文中分析位置地图的特点和服务模式,并基于地图空间认知理论,提出位置地图的空间认知过程,最后探讨位置地图的认知设计原则,为基于位置地图的位置服务中空间信息的认知与表达提供理论依据。     

A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling

Climate maps have been widely used for the construction of species distribution models. These maps derive from interpolation of data collected by meteorological stations. The sparse distribution of stations generates maps with coarse spatial resolution that are unable to detect microclimates or areas that can serve as plant or animal refuges. This work proposes a method for downscaling temperature maps using the solar radiation falling upon hillsides as predictor for the influence of relief on local variability. Solar irradiance is estimated from a digital elevation model of the study area using a routine based on analytical hillshading. Some examples of downscaling from 1 km to 25 m spatial resolution are shown. The results are compared with the surface temperature maps from Landsat 8 satellite imagery.     

Comparison of interpolation models for estimating heavy metals in soils under various spatial characteristics and sampling methods

Heavy metal pollution in soils has become increasingly challenging, especially in developing countries. Estimating the spatial distribution of heavy metals in soils is essential to preventing their build‐up. This article aims to identify the effects of spatial scales, spatial autocorrelation, sampling methods, and proportion on interpolation models in estimating the distribution of heavy metals in soils. Six interpolation models (area‐and‐point kriging, AAPK; inverse distance weighting, IDW; local polynomial interpolation, LP; ordinary kriging, OK; simple kriging, SK; and thin plate spline, TPS), three sampling methods (random, stratified, and systematic sampling), and five sampling proportions (1, 5, 10, 15, and 20%) are considered in this study using sets of simulated data, and the real situation was tested for verification. The results show that, in general, with the increase of spatial autocorrelation or the sampling percentage, the accuracy and stability of different interpolation models gradually increase; however, the various interpolation models have their own specific characteristics and application conditions. The best application conditions of the interpolation models compared with other models under the same situation are summarized and explained in theory. These conclusions have implications for future work.     

Spatial Modeling of the Black Death in Sweden

The purpose of this work is to determine whether spatial modeling can be used to model the spread of the Black Death. The study is limited to models for the propagation of the disease in Sweden in 1350. Geographic data of Swedish water bodies and medieval road networks, historical data on the population in Swedish parishes, including their medieval boundaries, along with historical notes and disease characteristics, were used to build alternative models for spatial distribution. Three different models are presented: one radial, one cost‐based and one combining network analysis and radial propagation. Simulations were made to depict different scenarios on the spread of the disease, as well as the drastic changes in the overall population of Sweden, over a couple of hundred years. For purpose of validation the population decrease estimated in each parish is compared with independent historical documents. Results from model scenarios are visualized in maps of propagation, animated video sequences and a web map service. Our analyses clearly demonstrate the power of spatial analysis and geographic information systems to describe, model and visualize epidemiologic processes in space and time.     

A GIS‐based Approach in Support of an Assessment of Food Safety Risks

A Geographical Information System (GIS)‐based approach was developed for the identification of vulnerabilities and the measurement of risks associated with contamination of food systems with biological agents. In this research work, a tight integration of ArcGIS with the Arena simulation tool has been implemented. Arena was used to simulate and track contamination in a food distribution network and transmit the time dependent information to GIS. ArcGIS was employed to provide the primary user interface, process network data, and visualize the results. In addition, the GIS, through its powerful capabilities to process spatial data, could allow decision‐ makers to quickly determine the potential impact of a contamination event, at any stage, as a function of both time and geography. Two contamination scenarios along the farm‐to‐fork chain were examined to show the geographic zone and the proportion of the population affected by the contamination. A constraint Voronoi data structure was developed to define influence zones (these were color coded according to a dynamic risk index), to identify those areas that are at greatest immediate risk as time progresses, and to estimate the population affected by these contamination events. This approach thus appears to have general application to many GIS‐based risk assessment problems.     

Inferring the home locations of Twitter users based on the spatiotemporal clustering of Twitter data

Residential locations play an important role in understanding the form and function of urban systems. However, it is impossible to release this detailed information publicly, due to the issue of privacy. The rapid development of location‐based services and the prevalence of global position system (GPS)‐equipped devices provide an unprecedented opportunity to infer residential locations from user‐generated geographic information. This article compares different approaches for predicting Twitter users' home locations at a precise point level based on temporal and spatial features extracted from geo‐tagged tweets. Among the three deterministic approaches, the one that estimates the home location for each user by finding the weighted most frequently visited (WMFV) cluster of that user always provides the best performance when compared with the other two methods. The results of a fourth approach, based on the support vector machine (SVM), are severely affected by the threshold value for a cluster to be identified as the home.     

Modeling Ambiguity in Census Microdata Allocations to Improve Demographic Small Area Estimates

This article describes a methodology for allocating demographic microdata to small enumeration areas such as census tracts, in the presence of underlying ambiguities. Maximum Entropy methods impute population weights that are constrained to match a set of census tract‐level summary statistics. Once allocated, the household characteristics are summarized to revise estimates of tract‐level demographic summary statistics, and to derive measures of ambiguity. The revised summary statistics are compared with original tract summaries within a context of expected variation. Allocation ambiguity is quantified for each household as a function of the distribution of imputed sample weights over all census tracts, and by computed metrics of confusion and variety of allocation to any census tract. The process reported here allows differentiation of households with regard to inherent ambiguity in the allocation decision. Ambiguity assessment represents an important component that has been neglected in spatial allocation work to date but can be seen as important additional knowledge for demographers and users of small area estimates. For the majority of tested variables, the revised tract level summaries correlate highly with original tract summary statistics. In addition to assessments for individual households, it is also possible to compute average allocation ambiguity for individual tracts, and to associate this with demographic characteristics not utilized in the allocation process.     

面向公平分配的时变应急服务覆盖优化

应急服务需要根据实际情况在城市的某些区位中提前布置,才能保证需求发生时能在规定时间内到达需求位置,提供应急服务。布置区位时,需要根据时变交通条件的变化,适当考虑时空的合理分布,以为城市居民提供较为公平的服务覆盖。传统最大区位覆盖模型较少考虑服务的公平分配,特别是在时变交通和复杂大规模路网条件下,缺乏对公平量度的显式建模。基于目前区域覆盖优化模型的最近进展,扩展了传统最大区位覆盖模型,建立面向公平分配的时变应急服务覆盖模型,提出了相应的启发式高效求解方法。利用武汉市数据进行实验验证,证实模型求解得到的区位配置解较之传统模型,不仅提供了更加公平的应急服务覆盖,也考虑到了时变交通条件,改善了应急资源在时空上的均衡配置水平,整体上提高了应急服务质量。     

土地利用优化配置建模研究进展与展望

土地利用优化配置是解决土地资源供需矛盾、协调社会经济发展与生态保护的重要技术手段,也是土地资源可持续利用需要解决的关键问题之一。随着空间模拟和智能优化技术的发展,土地利用优化的理论基础和方法体系也在不断更新。在文献计量的基础上,系统总结了国内外土地利用优化配置的驱动理论和方法,重点分析了空间模拟优化模型和智能寻优模型在土地利用优化配置建模应用中的进展和存在的问题,对土地利用优化配置未来的发展和面临的挑战进行了讨论和展望,提出自然与人文过程的耦合驱动、多模型的空间化协同、大数据知识挖掘方法的嵌入和数字孪生技术的支撑,应是土地利用优化配置领域下一阶段的研究方向。     

The Space Package: Tight Integration between Space and Semantics

Interpretation of spatial features often requires combined reasoning over geometry and semantics. We introduce the Space package, an open source SWI‐Prolog extension that provides spatial indexing capabilities. Together with the existing semantic web reasoning capabilities of SWI‐Prolog, this allows efficient integration of spatial and semantic queries and provides an infrastructure for declarative programming with space and semantics. There are few systems that provide indexing and reasoning facilities for both spatial and semantic data. A common solution is to combine separate semantic reasoning and geospatial services. Such loose coupling has the disadvantage that each service cannot make use of the statistics of the other. This makes optimization of such a service‐oriented architecture hard. The SWI‐Prolog Space and Semantic web packages provide a native Prolog interface to both spatial and semantic indexing and reasoning, which makes it easy to write combined query optimizers. Another advantage of the Space package is that it allows declarative logic programming, which means in practice that you say what you want to compute instead of how to compute it. The actual indexing machinery is encapsulated inside Prolog predicates. In this article we describe the interface of the Space package, compare its functionality to alternative software libraries, and show how to work with it using three example applications. These example illustrations include reasoning over movement patterns, dynamically loading geospatial linked data off the semantic web, and setting up a simple KML server.