Estimating spatial efficiency using cyber search,GIS, and spatial optimization: a case study of fire service deployment in Los Angeles County

ABSTRACT

The efficiency of public investments and services has been of interest to geographic researchers for several decades. While in the private sector inefficiency often leads to higher prices, loss of competitiveness, and loss of business, in the public sector inefficiency in service provision does not necessarily lead to immediate changes. In many cases, it is not an entirely easy task to analyze a particular service as appropriate data may be difficult to obtain and hidden in detailed budgets. In this paper, we develop an integrative approach that uses cyber search, Geographic Information System (GIS), and spatial optimization to estimate the spatial efficiency of fire protection services in Los Angeles (LA) County. We develop a cyber-search process to identify current deployment patterns of fire stations across the major urban region of LA County. We compare the results of our search to existing databases. Using spatial optimization, we estimate the level of deployment that is needed to meet desired coverage levels based upon the location of an ideal fire station pattern, and then compare this ideal level of deployment to the existing system as a means of estimating spatial efficiency. GIS is adopted throughout the paper to simulate the demand locations, to conduct location-based spatial analysis, to visualize fire station data, and to map model simulation results. Finally, we show that the existing system in LA County has considerable room for improvement. The methodology presented in this paper is both novel and groundbreaking, and the automated assessments are readily transferable to other counties and jurisdictions.     

Detecting the order of spatial dependence via symbolic analysis

In this article, we have proposed a simple diagnostic statistical procedure for testing the order of dependence of a spatial process. The proposed test is of nonparametric nature, and it is able to deal with potential nonlinear spatial dependencies. An added value is that from a methodological point of view, the new test is based on symbolic dynamics and hence on information theory. We characterized the behavior of a symbolic entropy measure in the presence of spatial dependencies of order higher than 1. The good power performance of the new method in detecting higher order spatial lags is notable and gives rise to an expectation that it may form a suitable basis for constructive specification searches.     

An integrated approach for addressing geographic uncertainty in spatial optimization

There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data-quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This article develops an integrated approach to address data uncertainty in spatial optimization. We demonstrate that it is possible to characterize uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty. Empirical findings indicate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data.     

发展时态GIS的可操作性时空数据模型

当前GIS广泛采用的空间数据模型不足以满足时态GIS发展的需要。该文探讨时态GIS所需的、可操作性时空数据模型,并且以土地利用和交通发展的时空相互作用为例,说明如何设计和实施相关的时空数据模型,用于支持发展时态GIS。     

Geospatial data conflation: a formal approach based on optimization and relational databases

ABSTRACT

Geospatial data conflation is aimed at matching counterpart features from two or more data sources in order to combine and better utilize information in the data. Due to the importance of conflation in spatial analysis, different approaches to the conflation problem have been proposed ranging from simple buffer-based methods to probability and optimization based models. In this paper, I propose a formal framework for conflation that integrates two powerful tools of geospatial computation: optimization and relational databases. I discuss the connection between the relational database theory and conflation, and demonstrate how the conflation process can be formulated and carried out in standard relational databases. I also propose a set of new optimization models that can be used inside relational databases to solve the conflation problem. The optimization models are based on the minimum cost circulation problem in operations research (also known as the network flow problem), which generalizes existing optimal conflation models that are primarily based on the assignment problem. Using comparable datasets, computational experiments show that the proposed conflation method is effective and outperforms existing optimal conflation models by a large margin. Given its generality, the new method may be applicable to other data types and conflation problems.     

城镇产业布局基础空间信息数据库系统的设计与实现

建立城镇产业布局基础空间信息数据库的目的是避免城镇基础空间数据集的重复采集,减少浪费,协调空间数据的使用,加强对信息资源有效而经济的管理。针对城镇产业布局分析中对基础数据的实际要求,综合利用地理信息系统技术、数据库技术和空间数据库引擎技术,设计了城镇产业布局基础空间信息数据库的系统框架结构和建库技术路线,并在此基础上建立了一套分布式城镇产业布局数据库原型系统。该系统能够对城镇产业布局分析相关的海量空间数据进行有效的组织、管理和应用,并预留了数据接口,可为相关的城镇产业布局分析软件提供数据支持服务。此外,该系统还可实现不同部门和用户的数据共享,为城镇建设决策提供数据支持。     

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena

Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.     

A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B_cont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. B_cont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (?0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.     

Geospatial Thinking and Spatial Ability: An Empirical Examination of Knowledge and Reasoning in Geographical Science

This study examined the relationships between geospatial thinking and spatial ability, focusing on knowledge and reasoning about fundamental concepts in geospatial science. Scores on a geospatial thinking test had low correlations with each other, compared to high correlations among scores on spatial ability tests, and the degrees of relationship between geospatial thinking and spatial ability depended on the type of geospatial thinking. Expert geospatial scientists did not outperform students on spatial ability tests, suggesting that expertise in geospatial science might not simply be explained by superiority in spatial ability. The results point to the necessity of targeted instruction for geospatial learning.     

Evaluation,simulation, and optimization of land use spatial patterns for high-quality development: A case study of Zhengzhou city,China

Journal of Geographical Sciences - High-intensity land use and resource overloaded-induced regional land use spatial pattern (LUSP) are essential and challenging for high-quality development. The...     

基于地理信息系统的2009-2013年甲型H1N1流感的时空分析

甲型H1N1流感作为一种人群普遍易感的急性传染病,自2009年爆发以来,不仅对人类健康造成了危害,也给全球经济造成了巨大损失。为研究中国大陆地区2009年来甲型H1N1流感疫情的时空分布特征,以中国大陆地区31个省级行政区2009-2013年甲型H1N1流感发病率的时空数据为研究对象,采用空间自相关分析技术和季节性分析方法进行了较为全面的空间和时间分析。结果表明：甲型H1N1流感的发病率在空间上和时间上都具有较强的聚集性。空间上,甲流发病高值热点有从新疆、青海和甘肃等西北部地区逐渐南移至中国东南部沿海省份的趋势,且高发聚集省份有增多的趋势;时间上,每年10月至次年4月是中国甲流的高发时段,且在此期间空间上也存在较强的聚集性。     

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data

Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data.     

The spatial distribution and factors affecting karst cave development in Guizhou Province

This research examines the distribution features of 4960 caves across Guizhou Province, while probing the relationship between the caves’ spatial patterns and geographic elements. This study is based on hydrogeological and topographic maps of Guizhou. ArcGIS software was used to process the adjacent index, spatial analysis, and coupling analysis of the caves altitude and longitude, as well as the rock properties, lithology, drainage and tectonic division of almost 5000 caves. Based on a point pattern analysis of Guizhou caves, the adjacent index is 0.53, and the coefficient of variation verified by Tyson polygon reached 72.469%. This figure reflects the clustered distribution pattern of the caves. Across the entire province, caves are divided into four concentrated areas and one weakly affected area. The four concentrated areas are Zunyi-Tongren, Bijie, Qianxinan-Liupanshui, and Guiyang- Anshun-Qinan. The one weakly affected zone is Qiandongnan. The most concentrated among them is the Guiyang-Anshun-Qiannan area, which covers 24.67% of the total province area, and accounts for 36.63% of the total province’s caves. Cave distribution in Guizhou is characterized as dense in the western part and sparse in the eastern part. Under this study background, the natural elements of formation, including lithology, structure, climate, hydrology, and altitude, and their effects on the distribution, number, and spatial pattern of cave development is analyzed.     

Wireless Mapping,GIS, and Learning about the Digital Divide: A Classroom Experience

Abstract

The purpose of this article is to describe a capstone course in undergraduate student geographical research in which GIS and other geospatial tools were used to teach undergraduate students basic geographical principles. The course uses the “cooperative learning” pedagogical approach to address one of a number of client-supplied research projects, chosen on the basis of logistical difficulty, time, student ability, and project importance. In the connection of primary data with existing data, students confronted a number of important research issues such as mapping ethics, database design and management, time management, group dynamics, and research limitations.     

基于GIS的山区土地资源安全定量评价模型 ——以四川省凉山州为例

土地资源是制约山区资源环境承载力的关键因素,土地资源安全状况在一定程度上决定了山区的资源环境安全状况和社会经济发展水平。在分析山区土地资源安全影响因素的基础上,论文构建了以建设用地安全指数和粮食安全指数为基础的山区土地资源安全评价模型。该模型使用城镇化率作为建设用地安全指数的权重,以反映建设用地安全在区域土地资源安全中的重要性;使用非城镇化率作为粮食安全指数的权重,以反映农村土地安全状态在土地资源安全中的重要性。在GIS 软件支持下,利用该模型对四川省凉山州各县市的土地资源安全指数进行了评估计算,结果表明凉山州土地资源安全情况总体不容乐观,土地资源安全等级为安全和较安全的县市仅有3 个,占全州县市总数的县市总数的17.65%,国土面积的17.23%;而土地资源不安全与临界安全的县就有10 个,占全州县市总数的58.82%,国土面积的55.03%。土地资源安全主要受地形条件约束,地形起伏度每下降0.09,土地资源安全指数约上升0.97。同时,土地资源不安全区与贫困区具有高度叠加的特点,这表明土地资源是主导山区发展和山区资源环境安全的关键因素,提高山区土地生产潜力,节约集约利用土地是保障山区土地资源安全及资源环境安全的重要措施。     

田野考古地理信息系统研究与建设

基于地理信息系统(GIS)技术建立“田野考古地理信息系统”(FAGIS),为田野考古发掘、研究以及文化遗产保护提供了一种数字化的手段与方法。针对用户“个性化服务”的应用需求,进行了系统柔性平台的框架设计,并分别介绍了基于摄影测量技术的数据采集与处理、考古知识获取与管理、空间决策支持、体视化与虚拟考古环境构建四个主要功能及其研究与实现的途径。     

Identifying Skill Requirements for GIS Positions: A Content Analysis of Job Advertisements

This study identifies the skill requirements for geographic information system (GIS) positions, including GIS analysts, programmers/developers/engineers, specialists, and technicians, through a content analysis of 946 GIS job advertisements from 2007–2014. The results indicated that GIS job applicants need to possess high levels of GIS analysis and modeling skills (e.g., database development) and personal and social skills (e.g., communication) to obtain employment, although specific skill requirements differed significantly depending on job title. The findings from this study may be useful for improving GIS majors’ employability.     

Metadata and provenance for spatial analysis: the case of spatial weights

Within a CyberGIS environment, the development of effective mechanisms to encode metadata for spatial analytical methods and to track the provenance of operations is a key requirement. Spatial weights are a fundamental element in a wide range of spatial analysis methods that deal with testing for and estimating models with spatial autocorrelation. They form the link between the data structure in a GIS and the spatial analysis methods. Over time, the number of formats for spatial weights implemented in software has proliferated, without any standard or easy interoperability. In this paper, we propose a flexible format that provides a way to ensure interoperability within a cyberinfrastructure environment. We illustrate the format with an application of a spatial weights web service, which is part of an evolving spatial analytical workbench. We describe an approach to embed provenance in spatial weights structures and illustrate the performance of the web service by means of a number of small experiments.     

A cyber-enabled spatial decision support system to inventory Mangroves in Mozambique: coupling scientific workflows and cloud computing

Mangroves are an important terrestrial carbon reservoir with numerous ecosystem services. Yet, it is difficult to inventory mangroves because of their low accessibility. A sampling approach that produces accurate assessment while maximizing logistical integrity of inventory operation is often required. Spatial decision support systems (SDSSs) provide support for integrating such a sampling design of fieldwork with operational considerations and evaluation of alternative scenarios. However, this fieldwork design driven by SDSS is often computationally intensive and repetitive. In this study, we develop a cyber-enabled SDSS framework to facilitate the computationally challenging fieldwork design that requires the efficacious selection of base camps and plots for the inventory of mangroves. Our study area is the Zambezi River Delta, Mozambique. Cyber-enabled capabilities, including scientific workflows and cloud computing, are integrated with the SDSS. Scientific workflows enable the automation of data and modeling tasks in the SDSS. Cloud computing offers on-demand computational support for interoperation among stakeholders for collaborative scenario evaluation for the fieldwork design of mangrove inventory. Further, this framework allows for harnessing high-performance computing capabilities for accelerating the fieldwork design. The cyber-enabled framework provides significant merits in terms of effective coordination among science and logistical teams, assurance of meeting inventory objectives, and an objective basis to collectively and efficaciously evaluate alternative scenarios.