Testing the effects of positional uncertainty on spatial decision‐making

There is now increasing agreement that the uncertainty associated with spatial information should be represented to users in a manner that is comprehensive and unambiguous. To assist with this task, researchers have developed a variety of methods to portray spatial uncertainty. While there has been some testing of the effectiveness of these displays, the possible effects of such representations on decision‐making have not yet been thoroughly investigated. Indeed, studies from the psychological literature indicate that people do not always make the same decisions when presented with the same information, and they can also be sensitive to the effects of presentation, task, and context. This paper examines how the use of four different methods to represent positional uncertainty can affect spatial decision‐making. The authors found that extremely significant differences in participants' responses were exhibited, depending on the manner in which positional uncertainty was displayed, although little difference was observed in the ability of the participants to comprehend the four display methods. In addition, strong preferences were recorded for certain representations over others.     

The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades

Small-scale spatial events are situations in which elements or objects vary in such a way that temporal dynamics are intrinsic to their representation and explanation. Some of the clearest examples involve local movement, from conventional traffic modeling to disaster evacuation where congestion, crowding, panic, and related safety issues are key features. We propose that such events can be simulated using new variants of pedestrian model, which embody ideas about how behavior emerges from the accumulated interactions between small-scale objects. We present a model in which the event space is first explored by agents using ‘swarm intelligence’. Armed with information about the space, agents then move in unobstructed fashion to the event. Congestion and problems over safety are then resolved through introducing controls in an iterative fashion, rerunning the model until a ‘safe solution’ is reached. The model has been developed to simulate the effect of changing the route of the Notting Hill Carnival, an annual event held in west central London over 2 days in August each year. One of the key issues in using such simulation is how the process of modeling interacts with those who manage and control the event. As such, this changes the nature of the modeling problem from one where control and optimization is external to the model to one where it is intrinsic to the simulation.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

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.     

全球化背景下酒吧的地方性与空间性——以广州为例

在全球化背景下,以广州代表性酒吧为对象,援引第三空间理论和空间生产理论,采用质性研究方法,从消费地理视角解读酒吧的地方性和空间性,重新诠释地方理论以及人地关系认识论。研究表明,广州环市东酒吧的空间建构包括了一种本土对西方、当下本土对历史本土的再结构化过程。在空间实践-空间想象-再现空间三元一体的维度下,酒吧作为地方具有典型的反身性,酒吧影响着主体行为,行为主体构筑的权力话语也成为酒吧空间的意义构成,涉入酒吧消费空间的建构过程。作为一个具有主体性的消费空间,酒吧具有空间生产、空间消费和空间流动的特质。     

Evaluating the impact of visualization of risk upon emergency route-planning

ABSTRACT

This paper reports on a controlled experiment evaluating how different cartographic representations of risk affect participants’ performance on a complex spatial decision task: route planning. The specific experimental scenario used is oriented towards emergency route-planning during flood response. The experiment compared six common abstract and metaphorical graphical symbolizations of risk. The results indicate a pattern of less-preferred graphical symbolizations associated with slower responses and lower-risk route choices. One mechanism that might explain these observed relationships would be that more complex and effortful maps promote closer attention paid by participants and lower levels of risk taking. Such user considerations have important implications for the design of maps and mapping interfaces for emergency planning and response. The data also highlights the importance of the ‘right decision, wrong outcome problem’ inherent in decision-making under uncertainty: in individual instances, more risky decisions do not always lead to worse outcomes.     

A GeoAgent‐based framework for knowledge‐oriented representation: Embracing social rules in GIS

While current Geographic Information Systems (GISs) can represent observational spatial data well, they have limited capabilities in representing some non‐observational social elements and goal‐driven behaviours that can be important factors in a wide range of geographic issues. Such social components may include laws, regulations, polices, plans, culture, and customs, as well as their relations and interactions with the geographic environment at different scales. Getting beyond traditional data‐centred approaches, this research presents a knowledge‐oriented strategy in order to address these issues within a GIS context. We incorporate two major conceptual elements. First, extending from conventional agent notions and their geographic applications, geographic agents (GeoAgents) are considered as a basic representation component to specifically address social rules and goal‐driven behaviours that impact the Earth and environmental systems. Second, in order to incorporate GeoAgents with current space–time representation, a new conceptual representation framework, called ‘fields, objects, time, GeoAgents, and relations’ (FOTAR), is introduced to address the cross‐scale processes of both social and natural interactions. A Java‐based prototype, GeoAgent‐based Knowledge System (GeoAgentKS), is described to implement this framework by integrating agent technologies with multiple data and knowledge representation techniques, such as expert systems, concept maps, mathematical models, and geospatial databases. The application of this prototype in a case study is also presented, investigating scale‐dependent human–environment interactions under different emergency situations for community water systems in Central Pennsylvania, USA. In this case study, a systematic set of methodologies of knowledge acquisition, representation, and confirmation for constructing GeoAgents' knowledge bases by using expert systems were explored to formalize high‐level knowledge and social behaviours in the FOTAR‐based representations. The results show that the proposed conceptual representation framework is achievable at both implementation and application levels, and the prototype tool is demonstrated to be valuable in facilitating knowledge sharing, policymaking, municipal management, and decision‐making, especially for real‐world emergency management.     

Efficiently implementable algebra for distributed in-network spatial analysis

Existing sensor network query processors (SNQPs) have demonstrated that in-network processing is an effective and efficient means of interacting with wireless sensor networks (WSNs) for data collection tasks. Inspired by these findings, this article investigates the question as to whether spatial analysis over WSNs can be built upon established distributed query processing techniques, but, here, emphasis is on the spatial aspects of sensed data, which are not adequately addressed in the existing SNQPs. By spatial analysis, we mean the ability to detect topological relationships between spatially referenced entities (e.g. whether mist intersects a vineyard or is disjoint from it) and to derive representations grounded on such relationships (e.g. the geometrical extent of that part of a vineyard that is covered by mist). To support the efficient representation, querying and manipulation of spatial data, we use an algebraic approach. We revisit a previously proposed centralized spatial algebra comprising a set of spatial data types and a comprehensive collection of operations. We have redefined and re-conceptualized the algebra for distributed evaluation and shown that it can be efficiently implemented for in-network execution. This article provides rigorous, formal definitions of the spatial data types, points, lines and regions, together with spatial-valued and topological operations over them. The article shows how the algebra can be used to characterize complex and expressive topological relationships between spatial entities and spatial phenomena that, due to their dynamic, evolving nature, cannot be represented a priori.     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

Evaluating representation and scale error in the maximal covering location problem using GIS and intelligent areal interpolation

A common problem in location-allocation modeling is the error associated with the representation and scale of demand. Numerous researchers have investigated aggregation errors associated with using different scaled data, and more recently, error associated with the geographic representation of model objects has also been studied. For covering problems, the validity of using polygon centroid representations of demand has been questioned by researchers, but the alternative has been to assume that demand is uniformly distributed within areal units. The spatial heterogeneity of demand within areal units thus has been modeled using one of two extremes – demand is completely concentrated at one location or demand is uniformly distributed. This article proposes using intelligent areal interpolation and geographic information systems to model the spatial heterogeneity of demand within spatial units when solving the maximal covering location problem. The results are compared against representations that assume demand is either concentrated at centroids or uniformly distributed. Using measures of scale and representation error, preliminary results from the test study indicate that for smaller scale data, representation has a substantial impact on model error whereas at larger scales, model error is not that different for the alternative representations of the distribution of demand within areal units.     

Integration of linear and areal hierarchies for continuous multi-scale representation of road networks

Spatial data can be represented at different scales, and this leads to the issue of multi-scale spatial representation. Multi-scale spatial representation has been widely applied to online mapping products (e.g., Google Maps and Yahoo Maps). However, in most current products, multi-scale representation can only be achieved through a series of maps at fixed scales, resulting in a discontinuity (i.e., with jumps) in the transformation between scales and a mismatch between the available scales and users' desired scales. Therefore, it is very desirable to achieve smoothly continuous multi-scale spatial representations. This article describes an integrated approach to build a hierarchical structure of a road network for continuous multi-scale representation purposes, especially continuous selective omission of roads in a network. In this hierarchical structure, the linear and areal hierarchies are constructed, respectively, using two existing approaches for the linear and areal patterns in a road network. Continuous multi-scale representation of a road network can be achieved by searching in these hierarchies. This approach is validated by applying it to two study areas, and the results are evaluated by both quantitative analysis with two measures (i.e., similarity and average connectivity) and visual inspection. Experimental results show that this integrated approach performs better than existing approaches, especially in terms of preservation of connectivity and patterns of a road network. With this approach, efficient and continuous multi-scale selective omission of road networks becomes feasible.     

Regularities in Spatial Information Processing: Implications for Modeling Destination Choice

This paper investigates the determinants of spatial knowledge and how our knowledge of space varies according to geographic location. By using data on U.S. city names recalled at 22 test locations, a multivariate model of the information surface specific to each test location is calibrated. This model links the probability of a city being recalled from memory to its distance from the test site, its population size, its location with respect to other cities, and whether or not it is a state capital. The paper then suggests that these recall data provide information on spatial knowledge surfaces from which large-scale spatial choices, such as migration destinations, are made. Results from the analysis lend further evidence to the idea that spatial knowledge is stored and processed hierarchically and that individuals underrepresent information in large clusters. Consequently, the results have important implications for modeling any spatial behavior based on individuals' spatial information surfaces. In particular, the results cast further doubt on the validity of traditional large-scale spatial choice frameworks and lend support to the competing destinations hypothesis.     

Integrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters

The timely and secure evacuation of residents to nearby urban emergency shelters is of great importance during unexpected disaster events. However, evacuation and allocation of shelters are seldom examined as a whole, even though they are usually closely related tasks in disaster management. To conduct better spatial allocation of emergency shelters in cities, this study proposes a new method which integrates techniques of multi-agent system and multi-criteria evaluation for spatial allocation of urban emergency shelters. Compared with the traditional emergency shelter allocation methods, the proposed method highlights the importance of dynamic emergency evacuation simulations for spatial allocation suitability analysis. Three kinds of agents involved in evacuation and sheltering procedures are designed: government agents, shelter agents, and resident agents. Emergency evacuations are simulated based on the interactions of these agents to find potential problems, for example, time-consuming evacuation processes and road congestion. A case study in Jing’an District, Shanghai, China was conducted to demonstrate the feasibility of the proposed method. After three rounds of simulation and optimization, new shelters were spatially allocated and a detailed recommended plan of shelters and related facilities was generated. The optimized spatial allocation of shelters may help local residents to be evacuated more quickly and securely.     

Regularities in Spatial Information Processing: Implications for Modeling Destination Choice

This paper investigates the determinants of spatial knowledge and how our knowledge of space varies according to geographic location. By using data on U.S. city names recalled at 22 test locations, a multivariate model of the information surface specific to each test location is calibrated. This model links the probability of a city being recalled from memory to its distance from the test site, its population size, its location with respect to other cities, and whether or not it is a state capital. The paper then suggests that these recall data provide information on spatial knowledge surfaces from which large-scale spatial choices, such as migration destinations, are made. Results from the analysis lend further evidence to the idea that spatial knowledge is stored and processed hierarchically and that individuals underrepresent information in large clusters. Consequently, the results have important implications for modeling any spatial behavior based on individuals' spatial information surfaces. In particular, the results cast further doubt on the validity of traditional large-scale spatial choice frameworks and lend support to the competing destinations hypothesis.     

流域系统综合模拟与情景分析——自然地理综合研究的新范式?

在全球环境变化和经济快速发展的背景下,经济发展与资源环境保护之间的矛盾日益激化,为解决或减弱这一矛盾,管理者需要新的知识体系和科学的决策工具。自然地理综合研究以自然地理要素空间变化及交互过程为主要研究内容,肩负着提供管理者所需新知识体系和科学决策工具的责任。流域作为空间上相对封闭的自然地理单元,其相对独立性为管理者的决策提供了天然的空间单元。流域内各地理要素的空间分布及其过程的交互机理自然成为管理者解决日益激化的矛盾所需的新知识体系,而流域系统综合模拟恰恰为建立这样的知识体系提供了极为有效的研究方式;在管理者评价各种决策的成效时,必须知道各种决策所产生的经济和环境效益,基于流域系统综合模拟的情景分析为管理者提供了所需要的科学决策工具。因此,从解决经济发展与资源环境保护之间矛盾的角度出发,流域系统综合模拟与情景分析应该成为新时代背景下自然地理学综合研究的新范式。文章结合2个小流域情景分析的研究案例,探讨了以流域系统综合模拟与情景分析为核心的现代自然地理综合研究需要解决的科学挑战,即流域系统综合模拟的系统化、空间化、定量化、易用化和决策化。     

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.     

基于指数计算的开发强度空间评价模型——以越南海岸线为例（英文）

Coastal zones play a major role in the conservation of marine ecosystems and the sustainable use of resources not only because of their special geographical environment but also because of their high temporal and spatial variability. With the development of urbanization, the exploitation and utilization of coasts have become important issues in the debate. To evaluate variations in the intensity of the land resource exploitation of coastal zones, an index-based model has been proposed in this paper, and coastal Vietnam has been established as the study area. The model is based on four normalized indexes to realize rapid evaluation of the spatial distribution of the exploitative intensity after zoning. The model was established to characterize the different exploitative intensities in different segments of the coast and to graphically present a sequence of decision choices for decision-makers. The results are as follows.(1) The simplicity and rapidity of the index operations can address the fast-changing characteristics of coastal exploitation and meet the desired precision.(2) The choices of the landward buffers fit well with the banded characteristics of the coastal zone. The buffers are horizontally divided into equidistant subregions, which can quantify the spatial differentiation of the exploitative intensity along the coast and perpendicular to the coast.(3) The average exploitative intensity is low, and the proportion of area that is to be exploited accounts for approximately 50%.Considering its spatial variation from north to south, the land exploitative intensity in the north is higher than that in the south. Compared to the intensity of land resource exploitation in the 20 km and 10 km buffers, the land exploitative intensity in the 5 km buffer is higher. The state of the intensity of land resource exploitation and how it can be used by stakeholders to manage coastal resources are then discussed.     

PLACE INVADERS: CONSTRUCTING THE NOMADIC THREAT IN ENGLAND*

Over the centuries, the image of nomads threatening sedentary ways of life has been a common pejorative representation. In order to understand what geographies underpin narratives about nomads, I examine how social theory and media representations invoke the image of nomads. Both media and academic representations are buttressed by limited understandings of place and space, framing nomads as the quintessential “place invaders.” Focusing on nomadic Gypsies and Travelers in England provides a contemporary example of this process. British media representations construct nomadic Gypsies and Travelers in England as out‐of‐place and threatening. Deconstructing essentialist geographical conceptions allows us to avoid reproducing the common image of placeless nomads, reveals how people utilize place to render others inferior, and highlights the fact that conflicts between nomadic and sedentary ways of life are not intractable and natural. Adopting a more nuanced understanding of place can challenge the dominant trope of nomads as place invaders.     

An index-based spatial evaluation model of exploitative intensity: A case study of coastal zone in Vietnam

Coastal zones play a major role in the conservation of marine ecosystems and the sustainable use of resources not only because of their special geographical environment but also because of their high temporal and spatial variability. With the development of urbanization, the exploitation and utilization of coasts have become important issues in the debate. To evaluate variations in the intensity of the land resource exploitation of coastal zones, an index- based model has been proposed in this paper, and coastal Vietnam has been established as the study area. The model is based on four normalized indexes to realize rapid evaluation of the spatial distribution of the exploitative intensity after zoning. The model was established to characterize the different exploitative intensities in different segments of the coast and to graphically present a sequence of decision choices for decision-makers. The results are as follows. (1) The simplicity and rapidity of the index operations can address the fast-changing characteristics of coastal exploitation and meet the desired precision. (2) The choices of the landward buffers fit well with the banded characteristics of the coastal zone. The buffers are horizontally divided into equidistant subregions, which can quantify the spatial differentiation of the exploitative intensity along the coast and perpendicular to the coast. (3) The average exploitative intensity is low, and the proportion of area that is to be exploited accounts for approximately 50%.Considering its spatial variation from north to south, the land exploitative intensity in the north is higher than that in the south. Compared to the intensity of land resource exploitation in the 20 km and 10 km buffers, the land exploitative intensity in the 5 km buffer is higher. The state of the intensity of land resource exploitation and how it can be used by stakeholders to manage coastal resources are then discussed.     

Extending the conversation on socially engaged geographic visualization: representing spatial inequality in Buffalo,New York

This paper is situated at the intersections among GIS and geovisualization, critical social theory, and urban studies. It presents an analysis of housing segregation and unequal food and transportation access in Buffalo, New York. We demonstrate how the representation and examination of this socially complex multi-scalar issue benefits from deliberate, reflexive conversation between different critical social-spatial epistemologies. We begin with a relatively simple GIS analysis of spatial segregation and arrive through critical iteration at a more qualitatively nuanced cartogram which moves beyond representations of fixed space to reveal a much more relational situation—a case of “time-space expansion” in which the travel time needed to meet a basic daily need is much greater for the poor and people of color than it is for whiter, more affluent populations. We conclude by infusing this narrative with additional considerations from social theory to show how even a limited visualization such as ours might better critically engage broader social and discursive processes in and across urban space.