Integration of linear programming and GIS for land-use modelling

Abstract

Geographical Information Systems (GIS) are becoming basic tools for a wide variety of earth science and land-use applications. This article presents linear programming (LP) as a promising tool for spatial modelling within a GIS. Although LP is not properly a spatial technique, it may be used to optimize spatial distributions or to guide the integration of variables. An example of the use of LP in land-use planning is described, with minimizing rural unemployment as the main goal. Technical, financial and ecological constraints are established to show the influence of several limitations on achieving the optimal solution. LP makes it possible to achieve optimal land-use, where the objective is maximized and the constraints respected. LP can also be used to simulate different planning scenarios, by modifying both the objective function coefficients and the constraints. The integration of LP and GIS is presented in two phases: (i) acquisition of attribute data for the LP model, and (ii) modelling and mapping the results.     

GIS and spatial analysis in the media

It is not uncommon to see maps and more advanced statistical or spatial analysis reported in the media. Geographical information systems (GIS) have no doubt facilitated access to and analysis of spatial information. This article is the outgrowth of contact by the Boston Globe and subsequent published articles examining structure fires and the location of fire stations in Massachusetts. The questions asked by the Boston Globe are important geographical ones regarding location specific urban/rural service provision issues, and to answer these questions requires the use of spatial analysis. This article focuses on the use of methods for answering the questions raised in the context of applying academic research, an issue that continues to be a challenge.     

Multi-criteria, multi-objective and uncertainty analysis for agro-energy spatial modelling

The worldwide increase in the use of biomass as a Renewable Energy Source raises the issue of introducing crops dedicated to energy production into rural landscapes. The purpose of this paper is to set-up a GIS based multi-criteria approach to assess a range of possibilities for perennial energy crops conversion. The presented method was implemented at the regional level in the Yorkshire and the Humber Region in Northern UK. The first phase of the study aims to set-up a land capability model for the specific purpose of assessing the potential of different typologies of perennial energy crops, on the basis of specific pedo-climatic and topographic factors. The model output illustrates a range of potentials for energy crop conversion that can be explored in the given landscape. In the second phase a uncertainty analysis of the land capability model was performed through a simulation approach in order to interpret the influence of assumptions and uncertainty on input data and model parameters. The last phase of the study allows allocating the energy crop conversion area according to specific environmental constraints, nature protection targets, food production priorities and land capability values. The land allocation output gives a rather restrictive energy crop penetration scenario, where more than half of the conversion area is allocated to cropping systems with low land degradation potential. This scenario represents a preliminary regional analysis of the energy crop potential in terms of theoretically available conversion areas. The final results also show that the areas with highest environmental risks correspond to the areas with both the lowest suitability for energy crop cultivation and the highest model uncertainty.     

Integrating GIS components and spatial statistical analysis in DBMSs

The integration of GIS and spatial statistical analysis can occur in two different ways: embedding spatial statistical analysis into a GIS environment, or embedding selected GIS functions into a spatial statistical analysis environment. Attention has been given almost exclusively to the former in the past. This paper argues that the latter deserves more attention, especially with the rapid development of embeddable GIS component technology. However, because current statistical software packages have not yet provided the environment into which GIS components can be embedded, we explore the possibility of integrating GIS components and spatial statistical analysis in a current proprietary Database Management System (DBMS: Microsoft Access), which can be easily extended to incorporate statistical analysis capabilities. A conceptual framework for this integration is outlined, an interactive spatial statistical analysis module is presented, and an example is used to illustrate the advantage of this module.     

Integrating GIS and spatial data analysis: problems and possibilities

Abstract

This article is an agreed summary of a workshop held in Sheffield between 18-20 March 1991. The focus here is on three of the themes of the workshop: the mutual benefits of closer links between geographical information systems (GIS) and the methods of spatial data analysis (SDA); the specific areas of SDA that should be linked with GIS; how the linkage should be made in practice. Directions for future research are also reviewed. The emphasis throughout is on statistical SDA and principally from the perspective of human rather than physical geography.     

UK-based terrorists' antecedent behavior: A spatial and temporal analysis

Background and purposeTerrorism is a real and present danger. The build-up to an attack includes planning, travel, and reconnaissance which necessarily require the offender to move through their environment. Whilst research has examined patterns of terrorist attack locations, with a few exceptions (e.g. Rossmo & Harries, 2011), it has not examined the spatial behavior of the terrorists themselves. In this paper, we investigate whether the spatial mobility patterns of terrorists resemble those of criminals (and the wider population) and if these change in the run up to their attacks.MethodUsing mobile phone data records for the ringleaders of four different UK-based terrorist plots in the months leading up to their attacks, we examine the frequency with which terrorists visit different locations, how far they travel from key anchor points such as their home, the distance between sequential cell-site hits and how their range of movement varies as the planned time to attack approaches.ConclusionsLike the wider population (and criminals), the sample of terrorists examined exhibited predictable patterns of spatial behavior. Most movements were close to their home location or safe house, and they visited a relatively small number of locations most of the time. Disaggregating these patterns over time provided mixed evidence regarding the way in which their spatial activity changed as the time to the planned attack approached. The findings are interpreted in terms of how they inform criminological understanding of the spatial behavior of terrorists, and the implications for law enforcement.     

Aoristic analysis: the spatial interpretation of unspecific temporal events

Temporal limitations of GIS databases are never more apparent than when the time of a change to any spatial object is unknown. This paper examines an unusual type of spatiotemporal imprecision where an event occurs at a known location but at an unknown time. Aoristic analysis can provide a temporal weight and give an indication of the probability that the event occurred within a defined period. Visualisation of temporal weights can be enhanced by modifications to existing surface generation algorithms and a temporal intensity surface can be created. An example from burglaries in Central Nottingham (UK) shows that aoristic analysis can smooth irregularities arising from poor database interrogation, and provide an alternative conceptualisation of space and time that is both comprehensible and meaningful.     

Integrating spatial and temporal contexts into a factorization model for POI recommendation

Matrix factorization is one of the most popular methods in recommendation systems. However, it faces two challenges related to the check-in data in point of interest (POI) recommendation: data scarcity and implicit feedback. To solve these problems, we propose a Feature-Space Separated Factorization Model (FSS-FM) in this paper. The model represents the POI feature spaces as separate slices, each of which represents a type of feature. Thus, spatial and temporal information and other contexts can be easily added to compensate for scarce data. Moreover, two commonly used objective functions for the factorization model, the weighted least squares and pairwise ranking functions, are combined to construct a hybrid optimization function. Extensive experiments are conducted on two real-life data sets: Gowalla and Foursquare, and the results are compared with those of baseline methods to evaluate the model. The results suggest that the FSS-FM performs better than state-of-the-art methods in terms of precision and recall on both data sets. The model with separate feature spaces can improve the performance of recommendation. The inclusion of spatial and temporal contexts further leverages the performance, and the spatial context is more influential than the temporal context. In addition, the capacity of hybrid optimization in improving POI recommendation is demonstrated.     

The resource boom and socio-economic well-being in Australian resource towns: a temporal and spatial analysis

The economy of Western Australia has long been characterized by a heavy dependence on extractive industries. The past decade, however, has seen the mining industry expand very rapidly, with significant implications for the small towns that support the sector. In this article, we consider the socio-economic performance of these towns through an assessment of unemployment, welfare dependence and incomes. In contrast to many other studies of resource boomtowns that typically focus on a single locality or time period, in this study we focus explicitly on spatial and temporal variability. We examine the ways in which place-specific characteristics—the commodity produced, local economic diversity and basic demographic features of a town—interact and have contributed to change in socio-economic well-being of mining communities across Western Australia over a 10-year period.     

A unified approach for location-allocation analysis: integrating GIS,distributed computing and spatial optimization

Location-allocation modeling is an important area of research in spatial optimization and GIScience. A large number of analytical models for location-allocation analysis have been developed in the past 50 years to meet the requirements of different planning and spatial-analytic applications, ranging from the location of emergency response units (EMS) to warehouses and transportation hubs. Despite their great number, many location-allocation models are intrinsically linked to one another. A well-known example is the theoretical link between the classic p-median problem and coverage location problems. Recently, Lei and Church showed that a large number of classic and new location models can be posed as special case problems of a new modeling construct called the vector assignment ordered median problem (VAOMP). Lei and Church also reported extremely high computational complexity in optimally solving the best integer linear programming (ILP) formulation developed for the VAOMP even for medium-sized problems in certain cases.

In this article, we develop an efficient unified solver for location-allocation analysis based on the VAOMP model without using ILP solvers. Our aim is to develop a fast heuristic algorithm based on the Tabu Search (TS) meta-heuristic, and message passing interface (MPI) suitable for obtaining optimal or near-optimal solutions for the VAOMP in a real-time environment. The unified approach is particularly interesting from the perspective of GIScience and spatial decision support systems (DSS) as it makes it possible to solve a wide variety of location models in a unified manner in a GIS environment. Computational results show that the TS method can often obtain in seconds, solutions that are better than those obtained using the ILP-based approach in hours or a day.     

新疆植被覆盖度趋势演变实验性分析

基于MODIS-NDVI数据,提取新疆2005-2015年植被覆盖度（FVC）。通过依据海拔和植被覆盖度的指标划分出山地、绿洲、平原、荒漠等11个子系统。通过斜率、变异系数、线性回归模型等方法来对全疆和不同生态分区的现状和未来发展趋势进行分析,并用BP人工神经网络来预测新疆2016-2020年的植被覆盖度的时空变化和分析2005-2020年时空动态变化趋势。主要结论为：① 新疆植被覆盖度总体为上升趋势,从西北向东南逐渐下降;山地呈逐年上升趋势,荒漠呈不显著退化趋势。植被覆盖度的变化主要是由降水量的变化引起;② 在整个新疆的荒漠和绿洲边缘构成了一个“绿洲—荒漠改善过渡带”,绿洲呈明显的改善趋势;③ 2009年是研究期内多数分区植被覆盖度的历史最低点;④ 在山脉的冰川积雪、湖泊周围的变异性很大,范围在150%~316%之间,这主要是由于气候变化、冰川消融和湖泊水位的波动变化所致;⑤ 北疆生态明显好于东疆与南疆,其绿洲区域呈现明显的改善趋势。伊犁地区的植被覆盖度相比于其他3个分区的变幅很大,山地区域呈明显的逐年退化趋势。伊犁地区植被覆盖度的局部最低点是在2008年,比其他分区的2009年提前了一年,相应的存在“实时”（伊犁）和“滞后”（东疆、南疆和北疆）的效应,主要是由于降水量和气温的变化所致。     

An inductive modelling procedure based on Bayes' theorem for analysis of pattern in spatial data

Abstract

This paper describes an inductive modelling procedure integrated with a geographical information system for analysis of pattern within spatial data. The aim of the modelling procedure is to predict the distribution within one data set by combining a number of other data sets. Data set combination is carried out using Bayes’ theorem. Inputs to the theorem, in the form of conditional probabilities, are derived from an inductive learning process in which attributes of the data set to be modelled are compared with attributes of a variety of predictor data sets. This process is carried out on random subsets of the data to generate error bounds on inputs for analysis of error propagation associated with the use of Bayes’ theorem to combine data sets in the GIS. The statistical significance of model inputs is calculated as part of the inductive learning process. Use of the modelling procedure is illustrated through the analysis of the winter habitat relationships of red deer in Grampian Region, north-east Scotland. The distribution of red deer in Deer Management Group areas in Gordon and in Kincardine and Deeside Districts is used to develop a model which predicts the distribution throughout Grampian Region; this is tested against red deer distribution in Moray District. Habitat data sets used for constructing the model are accumulated frost and altitude, obtained from maps, and land cover, derived from satellite imagery. Errors resulting from the use of Bayes’ theorem to combine data sets within the GIS and introduced in generalizing output from 50 m pixel to 1 km grid squares resolution are analysed and presented in a series of maps. This analysis of error trains is an integral part of the implemented analytical procedure and provides support to the interpretation of the results of modelling. Potential applications of the modelling procedure are discussed.     

Providing spatial statistical data analysis functionality for the GIS user: the SAGE project

Geographic representation has become more complex through time as researchers have added new concepts, leading to apparently endless proliferation and creating a need for simplification. We show that many of these concepts can be derived from a single foundation that we term the atomic form of geographic information. The familiar concepts of continuous fields and discrete objects can be derived under suitable rules applied to the properties and values of the atomic form. Fields and objects are further integrated through the concept of phase space, and in the form of field objects. A second atomic concept is introduced, termed the geo‐dipole, and shown to provide a foundation for object fields, metamaps, and the association classes of object‐oriented data modelling. Geographic dynamics are synthesized in a three‐dimensional space defined by static or dynamic object shape, the possibility of movement, and the possibility of dynamic internal structure. The atomic form also provides a tentative argument that discrete objects and continuous fields are the only possible bases for geographic representation.     

Multi-criteria modelling and clustering of spatial information

To improve the formal integration of verbally given spatial information in a geographical information system, a methodology for converting such information to a geographical representation was developed. This was applied to examples of a spatial-related analysis of natural language text messages in the domain of disaster management. This article presents an approach for such a conversion by using a suitable knowledge representation as well as formal modelling structures. The structures are provided by an ontology-supported knowledge base. With respect to spatial uncertainty, a formal representation based on possibility theory was defined. Moreover, a cluster algorithm was developed for handling information of different sources and building a spatial context.     

Sedimentary diatoms along a temporal and spatial gradient of metal contamination

We examined changes at the community and population level of sedimentary diatoms over a wide temporal and spatial gradient of metal pollution encountered in cores from three lakes in the Abitibi mining region (Québec, Canada). Diatom communities on the whole appeared to be very tolerant of metal contamination, as shown by diatom cell accumulation rates decreasing only under the most severe conditions of contamination, which were found from the 1930s to the 1980s in Lac Dufault (cadmium, up to 94 μg/g dry sediment; Cu, up to 8600 μg/g; Zn, up to 9000 μg/g). Under the moderate conditions of contamination observed in the other two lakes and in the most recent sediment of Lac Dufault, diatom cell accumulation rates tended to increase over values typical of the pre-mining period. However, there were increasing rearrangements of the community composition along the contamination gradient. Under moderate metal enrichment, the diatom community of Lac Vaudray experienced only subtle changes, with Cyclotella stelligera, albeit decreasing, remaining the dominant taxon. In the intermediately contaminated Lac Caron, several benthic taxa, noticeably Cymbella silesiaca and several Fragilaria species, rose in taxonomic importance. The most extreme contamination observed in Lac Dufault led to a severely impoverished community almost entirely represented by Achnanthes minutissima and Brachysira vitrea. With increasing levels of contamination, there was a shift from planktonic to benthic taxa and morphotypes and a consistent decrease in the siliceous stomatocysts/diatom frustules ratio. These trends suggest that littoral zones may represent an important refugium under conditions of high contamination. Responses to metal stress were in general more evident at the population than at the community level. Cyclotella stelligera and B. vitrea had a consistent negative and positive response, respectively, along the gradient and are the most promising indicators of metal pollution for this region.     

Evaluating field-based GIS for environmental characterization,modelling and decision support

ABSTRACT

This paper examines the potential of using GIS (Geographical Information Systems) in the field for environmental characterization, modelling and decision support particularly in isolated areas where data collection is difficult. Observations are based on experiences gained during two Anglo-Russian expeditions to the Altai Mountains of south central Siberia aimed at evaluating proposals for a new national park in the Katunsky Ridge area of the Belukha Massif. The use of GIS together with GPS (Global Positioning Systems) for primary data collection and verification/update of existing data is described and the use of field-based systems for on-the-spot environmental modelling and decision support is evaluated.     

An integration of GIS,virtual reality and the Internet for visualization,analysis and exploration of spatial data

This paper explores the way in which GIS, Virtual Reality (VR) and the Internet are closely integrated through the link of Virtual Reality Modelling Language (VRML) for spatial data visualization, analysis and exploration. Integration takes advantage of each component, and enables the dynamic 3D content to be built, visualized, interacted with and deployed all on the Web. To accomplish this, a hybrid approach that merges the conventional client-side and server-side methods is proposed, which offers the best of both worlds in terms of flexibility and capability, as well as the rational use of computing resources. Based on this approach, a Web-based prototype toolkit is designed and implemented by using an affordable desktop GIS through its macro language together with Java, Common Gateway Interface (CGI) and HTML programming. This toolkit comprises a 3D visualization tool, a 3D analysis tool, and a Java/VRML interface, which are respectively used for the creation of VRML models from 2D maps, surface analysis (e.g. profile creation and visibility analysis), and interaction (e.g. selecting and querying) with the output VRML worlds of 3D visualization and analysis. It is demonstrated that this toolkit provides an integrated environment, facilitating users to gain insights from the interaction with virtual environments that are built from existing GIS databases.     

Soil-landscape modelling and spatial prediction of soil attributes

Abstract

Explicit and quantitative models for the spatial prediction of soil and landscape attributes are required for environmental modelling and management. In this study, advances in the spatial representation of hydrological and geomorphological processes using terrain analysis techniques are integrated with the development of a field sampling and soil-landscape model building strategy. Statistical models are developed using relationships between terrain attributes (plan curvature, compound topographic index, upslope mean plan curvature) and soil attributes (A horizon depth, Solum depth, E horizon presence/absence) in an area with uniform geology and geomorphic history. These techniques seem to provide appropriate methodologies for spatial prediction and understanding soil landscape processes.     

基于GIS场模型的城市餐饮服务热点探测及空间格局分析

餐饮服务是城市生活的重要组成部分,提取城市餐饮服务热点并识别其空间分布模式,对于理解城市形态结构具有重要意义。针对过去基于POI进行城市形态特征定量分析的不足,利用GIS场模型对城市特征要素的空间分布模式进行识别,并采用地学信息图谱对其模式进行可视化和分析。以济南市主城区4.71万个餐饮服务POI作为主要数据源,首先基于密度场热点探测模型提取餐饮服务热点并按照密度值进行等级划分;然后采用广义对称结构图谱和数字场层次结构图谱表达餐饮服务热点的空间分布结构特征和规模等级结构特征,并构建其分布模式图谱;最后对结果展开讨论。研究表明：① 数字场热点探测模型能够有效地从POI中识别出不同等级的热点。② 广义对称结构图谱和基于GIS场模型的层级结构图谱能够分别从纵横两个方面分析和表达餐饮热点的空间分布结构和层次等级结构特征。综上所述,本研究为基于POI的城市特征要素提取和城市形态研究提供了一种有效的定量分析思路,其方法也可以推广至其他城市特征要素的提取、分析和表达当中。