GIS‐based multicriteria spatial modeling generic framework

Multicriteria analysis is a set of mathematical tools and methods allowing the comparison of different alternatives according to many criteria, often conflicting, to guide the decision maker towards a judicious choice. Multicriteria methods are used in spatial context to evaluate and compare spatial decision alternatives, often modeled through constraint‐based suitability analysis and represented by point, line, and polygon features or their combination, and evaluated on several space‐related criteria, to select a restricted subset for implementation. Outranking methods, a family of multicriteria methods, may be useful in spatial decision problems, especially when ordinal evaluation criteria are implied. However, it is recognized that these methods, except those devoted to multicriteria classification problems, are subject to computational limitations with respect to the number of alternatives. This paper proposes a framework to facilitate the incorporation and use of outranking methods in geographical information systems (GIS). The framework is composed of two phases. The first phase allows producing a planar subdivision of the study area obtained by combining a set of criteria maps; each represents a particular vision of the decision problem. The result is a set of non‐overlapping spatial units. The second phase allows constructing decision alternatives by combining the spatial units. Point, line and polygon feature‐based decision alternatives are then constructed as an individual, a grouping of linearly adjacent or a grouping of contiguous spatial units. This permits us to reduce considerably the number of alternatives, enabling the use of outranking methods. The framework is illustrated through the development of a prototype and through a step‐by‐step application to a corridor identification problem. This paper includes also a discussion of some conceptual and technical issues related to the framework.     

Quadtree storage of vector data†

Abstract

Vector data storage has various advantages in a cartographic or geographical information system (GIS) environment, but lacks internal spatial relationships between individual features. Quadtree structures have been extensively used to store and access raster data. This paper shows how quadtree methods may be adapted for use in spatially indexing vector data. It demonstrates that a vector quadtree stored in floating point representation overcomes the classical problem with raster quadtrees of data approximation. Examples of vector quadtrees applied to realistic size data sets are given     

Can a Simulated Flood Experience Improve Social Resilience to Disasters?

The increase of extreme meteorological phenomena, along with continuous population growth, has led to a rising number of flooding disasters. Therefore, there is an urgent need to develop better risk reduction strategies, among which is increased social resilience. Experiencing a disaster is recognized as a factor that positively influences overall community resilience, with particular effects on social resilience; it appears to be more influential than school education. It also has many negative effects, though. Previous studies underline that citizens do not distinguish between different types of experiences. Thus, we investigated whether a simulated experience of a flood can improve social resilience, without being hampered by negative repercussions. The study was executed in five municipalities in three Italian regions involved in the European project LIFE PRIMES, which planned simulation activities for each studied area. Data, collected through the administration of anonymous questionnaires before and after a flood drill, were processed by applying a multicriteria decision analysis tool (PROMETHEE). Results show that the drill significantly augmented perceived social resilience in the smaller studied communities but not in the larger ones, a fact that should be further investigated. Key Words: multicriteria decision analysis, simulated flood experience, social resilience to disasters.     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.     

GIS‐based multicriteria decision analysis: a survey of the literature

The integration of GIS and multicriteria decision analysis has attracted significant interest over the last 15 years or so. This paper surveys the GIS‐based multicriteria decision analysis (GIS‐MCDA) approaches using a literature review and classification of articles from 1990 to 2004. An electronic search indicated that over 300 articles appeared in refereed journals. The paper provides taxonomy of those articles and identifies trends and developments in GIS‐MCDA.     

Defining the Bull'S Eye: Satellite Imagery-Assisted Slum Population Assessment in Hyderabad,India

Abstract

This paper presents an approach to qualitative and spatial assessment of slum population numbers in Hyderabad, India using circle-based population data from the Census of India and results of the analysis of high resolution QuickBird satellite image data (2003) derived from automatic line detection and lacunarity algorithm. This approach provides plausible and spatially explicit aggregate statistics of slum population numbers within the city. This work suggests that both over- and underreporting of slum population numbers does occur in Hyderabad, and provides an improved view on the slum distribution patterns within this urban agglomeration.     

Measures of transport mode segmentation of trajectories

ABSTRACT

Rooted in the philosophy of point- and segment-based approaches for transportation mode segmentation of trajectories, the measures that researchers have adopted to evaluate the quality of the results (1) are incomparable across approaches, hence slowing the progress in the field and (2) do not provide insight about the quality of the continuous transportation mode segmentation. To address these problems, this paper proposes new error measures that can be applied to measure how well a continuous transportation mode segmentation model performs. The error measures introduced are based on aligning multiple inferred continuous intervals to ground truth intervals, and measure the cardinality of the alignment and the spatial and temporal discrepancy between the corresponding aligned segments. The utility of this new way of computing errors is shown by evaluating the segmentation of three generic transportation mode segmentation approaches (implicit, explicit–holistic, and explicit–consensus-based transport mode segmentation), which can be implemented in a thick client architecture. Empirical evaluations on a large real-word data set reveal the superiority of explicit–consensus-based transport mode segmentation, which can be attributed to the explicit modeling of segments and transitions, which allows for a meaningful decomposition of the complex learning task.     

Object-oriented knowledge-based symbol selection for visualizing statistical information

Abstract

Object-oriented schemes for representing and processing knowledge concerned with cartographic sy mbolization in GIS have been suggested by a number of researchers. As a sequel to these research efforts, the problem of object-oriented knowledge representation in the domain of symbol selection for visualizing spatial statistical information is addressed in this paper, and an operational system on this domain is presented. First, the domain knowledge is specified, and the decision process for symbol selection is formulated. Then the specified knowledge is represented and processed in an object-oriented manner. Implementation issues are discussed, and results are demonstrated with examples.     

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.     

从高维特征空间中获取元胞自动机的非线性转换规则

元胞自动机 (CA) 具有强大的空间模拟能力,能够模拟和预测复杂的地理现象演变过程。CA 的核心是如何定义转换规则,但目前CA转换规则获取往往是基于线性方法来进行,例如采用多准则判断 (MCE) 技术。这些方法较难反映地理现象所涉及的非线性等复杂特征。为此提出了利用新近发展的核学习机来获取地理元胞自动机非线性转换规则的新方法。该方法是通过核函数产生隐含的高维特征空间,把复杂的非线性问题转化成简单的线性问题,为解决复杂非线性问题提供了一种非常有效的途径。利用所提出的方法自动获取地理元胞自动机的转换规则,不仅大大减少了建模所需的时间,也较好地反映地理现象复杂的特性,从而改善了CA模拟的效果。     

Database design for a multi-scale spatial information system

Abstract

Growth in the available quantities of digital geographical data has led to major problems in maintaining and integrating data from multiple sources, required by users at differing levels of generalization. Existing GIS and associated database management systems provide few facilities specifically intended for handling spatial data at multiple scales and require time consuming manual intervention to control update and retain consistency between representations. In this paper the GEODYSSEY conceptual design for a multi-scale, multiple representation spatial database is presented and the results of experimental implementation of several aspects of the design are described. Object-oriented, deductive and procedural programming techniques have been applied in several contexts: automated update software, using probabilistic reasoning; deductive query processing using explicit stored semantic and spatial relations combined with geometric data; multiresolution spatial data access methods combining poini, line, area and surface geometry; and triangulation-based generalization software that detects and resolves topological inconsistency.     

Diffusion of geographic information innovations

Abstract

Appropriate diffusion of geographic information technologies is hampered by lack of systematic research on factors and processes affecting diffusion, utilization and impact assessment of the technologies and by a variety of conceptual and methodological problems. Diffusion of innovation principles developed in other fields, in combination with methods developed within the field of management information systems, provide an important beginning for improved understanding. This paper focuses on gaps in knowledge which might be addressed within the geographic information field by analysis techniques and research methodologies used in the diffusion of innovations.     

Web-based geospatial multiple criteria decision analysis using open software and standards

The emerging ubiquity of geospatial information is providing an unprecedented opportunity to apply Geographical Information Systems (GIS)-based multiple criteria decision analysis (MCDA) to a broad spectrum of use cases. Volunteered geographic information, open GIS software, geoservice-based tools, cloud-based virtualized platforms, and worldwide collaboration of both domain experts and general users have greatly increased the quantity and accessibility of geospatially referenced data resources. Currently, there is a lack of GIS-based MCDA tools that integrate this decision-driven process within a widely accessible, robust geoframework environment, designed for user-friendly interaction. In this contribution, we present a conceptual workflow and proof-of-concept software application, Geocentric Environment for Analysis and Reasoning (GEAR), which provides a viable transition path to enhance geospatial MCDA in the age of open GIS. We propose a Web-based platform that leverages open-source geotechnologies to incorporate a wide variety of geospatial data formats in a common solution space to allow for spatially enhanced and time-relevant decision analysis. Through the proposed workflow, a user can ingest and modify heterogeneous data formats, exploit temporally tagged data sources, create multicriteria decision analysis models, and visualize the results in an iterative and collaborative workspace. A sample case study applied to disaster relief is used to demonstrate the prototype and workflow. This proof-of-concept Web-based application provides a notional pathway of how to connect open-source data to open-source analysis through a geospatially enabled MCDA workflow that could be virtually accessible to many levels of decision makers from individuals to entire organizations.     

The Decision‐Making/Accountability Spatial Incongruence Problem for Research Linking Environmental Science and Policy

Increasingly, scholars engage policy makers around fundamental, complex questions on environmental change in interdisciplinary settings. Researchers attempting to develop robust contributions to knowledge that can support policymaker understandings in this context face significant inferential challenges in dealing with the spatial dimension of their phenomenon of interest. In this paper, we extend an understanding of well‐defined methodological challenges familiar to applied spatial scientists by explicitly articulating the Decision‐Making/Accountability, Spatial Incongruence Problem, or DASIP. Three case studies illustrate how spatial incongruences matter to researchers who work on complex, interdisciplinary problems, while seeking to understand decision‐making or policy‐related phenomenon: urban heat‐island mitigation research in Arizona, water transfer conflicts in Kansas, and hydraulic‐fracturing debates in Texas. With such examples, we aim to evoke a deeper understanding of this problem in applied research and also inspire thinking about how scholars might innovate methods for creating knowledge about environmental change that supports spatially accountable decision making.     

Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

ABSTRACT

We argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran’s I statistics estimated on 5-year ACS estimates (2006–2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data.     

Taxonomy for surveying the use and value of geographical information!

Abstract

Understanding the use and value of geographical information in decision making has been identified as crucial to the further development of geographical information systems and related spatial analysis techniques. Research into the use of geographical information will be through surveys and case studies. A taxonomy for investigating the use of geographical information and its associated value is needed to structure the surveys and case studies. This paper presents an initial attempt at such a taxonomy, intended to support continuing research into the use and value of geographical information. The survey methodology to be supported will be mostly telephone and/or mail questionnaires, and this has influenced the structure of the taxonomy. The goal of this work is to create a taxonomy suitable for use in surveys that will enhance the understanding of the use and value of geographical information in decision making.     

GeoVisuals: a visual analytics approach to leverage the potential of spatial videos and associated geonarratives

ABSTRACT

Videos embedded with spatial coordinates, especially when combined with additional expert insights, offer the potential to acquire fine-scale multi-time period contextualized data for a variety of different environments. However, while these geospatial multimedia (GSMM) data include abundant spatiotemporal, semantic and visual information, the means to fully leverage their potential using a suite of visual and interactive analysis techniques and tools has thus far been lacking. In this paper, we address this gap by first identifying the types of tasks required of GSMM data, and then presenting a solution platform. This GeoVisuals system utilizes a visual analysis approach built on semantic data points that can be integrated spatially, which in turn enables management in a unified database with combined spatio-temporal and text querying. A set of visualization functions are integrated in two investigation modes: geo-video analysis and geo-location analysis.     

Progress of research on urban-rural transformation and rural development in China in the past decade and future prospects

Urban-rural transformation and rural development are issues at the forefront of research on the topic of the urban-rural relationship in the field of geography, as well as important practical problems facing China’s new urbanization and overall planning of urban and rural development. The Center for Regional Agricultural and Rural Development, part of the Institute of Geographic Sciences and Natural Resources Research under the Chinese Academy of Sciences, was established in 2005. The Center has laid solid foundations for integrating research in the areas of agricultural geography and rural development in China over the past decade. The paper aims to review the major achievements in rural geographical research in China during the past decade, analyze innovative developments in relevant theories and methods, and suggest prospects and countermeasures for promoting comprehensive studies of urban-rural transformation and rural geography. The research shows that innovative achievements have been made in rural geography studies of China in the past decade as major national policy development, outputs of result and decision making support; new breakthroughs have been achieved in such major research projects as geographical integrated theory, land remediation projects and technology demonstration projects, new urbanization and urban-rural integration; significant progress has been made in actively expanding the frontiers of rural geography and pushing forward theoretical innovations in land and resource projects; and, with China’s development goals of building a moderately prosperous society in all respects and achieving modernization in mind, future innovative developments in agricultural and rural geography should aim to make research more strategic, systematic, scientific and security-oriented, with attention given to promoting systematic scientific research on international cooperation and global rural geography.