Using Linear Integer Programming for Multi‐Site Land‐Use Allocation

Research in the area of spatial decision support (SDS) and resource allocation has recently generated increased attention for integrating optimization techniques with GIS. In this paper we address the use of spatial optimization techniques for solving multi‐site land‐use allocation (MLUA) problems, where MLUA refers to the optimal allocation of multiple sites of different land uses to an area. We solve an MLUA problem using four different integer programs (IP), of which three are linear integer programs. The IPs are formulated for a raster‐based GIS environment and are designed to minimize development costs and to maximize compactness of the allocated land use. The preference for either minimizing costs or maximizing compactness has been made operational by including a weighting factor. The IPs are evaluated on their speed and their efficacy for handling large databases. All four IPs yielded the optimal solution within a reasonable amount of time, for an area of 8 × 8 cells. The fastest model was successfully applied to a case study involving an area of 30 × 30 cells. The case study demonstrates the practical use of linear IPs for spatial decision support issues.     

Using an Evolutionary Algorithm in Multiobjective Geographic Analysis for Land Use Allocation and Decision Supporting

Usually, allocation of resources is an optimization problem which involves a variety of conflicting economic, social, and ecological objectives. In such a process, advanced geographic analyst tool for manipulation of spatial data and satisfaction of multiple objectives is essential to the success of decision‐making. The present research intends to demonstrate the application of a multiobjective optimization method based on NSGA‐II 1 (we call it HNSGA‐II), along with Geographical Information System (GIS) 2 to select suitable sites for the establishment of large industrial units. Having defined the elements of HNSGA‐II for the site selection of industrial units, the method is tested on the data of Zanjan province, Iran, as the case study. The results showed that the proposed approach can easily find a variety of optimized solutions, giving the decision‐makers the possibility to opt for the most propitious solution. Using this method, the achievement level regarding each objective function can be studied for any of the nondominated solutions.     

A Bi‐objective Optimization Model for Designing Safe Walking Routes for School Children

This study uses a mathematical optimization approach to design safe walking routes from school to home for children. Children are thought to be safer when walking together in groups rather than alone. Thus, we assume that the risk of walking along a given road segment in a group is smaller than that of walking the same segment alone. At the same time, the walking route between school and home for each child should not deviate substantially from the shortest route. We propose a bi‐objective model that minimizes both the total risk (particularly, the total distance walked alone) and the total walking distance for all children. We present an integer programming formulation of the proposed problem and apply this formulation to two instances based on actual road networks. We obtain Pareto optimal solutions using a mathematical programming solver and analyze the characteristics of the solutions and their potential applicability to real situations. The results show that the proposed model produces much better solutions compared with the solution where each child walks along the shortest path from school to home. In some optimal solutions, only a small deviation from the shortest path results in a dramatic reduction of the risk objective.     

Optimal Allocation of Land between Productive Use and Recreational Use

Abstract This paper is an inquiry into the optimal allocation of time and natural areas to recreational uses, which have the feature of being a pure and continuous public good. We address this issue with a comprehensive approach. A static rational general equilibrium framework is developed in which heterogeneous agents allocate land and time endowments between alternative uses. This modeling has important advantages. First, Pareto‐optimal and voluntary‐contribution equilibrium allocations are obtained in a unified set‐up. Second, the suboptimality result of the decentralized equilibrium, the free‐rider problem on the provision of this nonexcludable public good, and different mechanisms to return the economy to its first‐best are analyzed. Finally, a methodological critique is made of some empirical literature, and it is suggested that our theoretical microeconomic‐based structure seems to be a suitable starting point for empirical research.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.     

A NEW SPATIAL MULTIPLE DISCRETE‐CONTINUOUS MODELING APPROACH TO LAND USE CHANGE ANALYSIS

This paper formulates a multiple discrete‐continuous probit (MDCP) land use model within a spatially explicit economic structural framework for land use change decisions. The spatial MDCP model is capable of predicting both the type and intensity of urban development patterns over large geographic areas, while also explicitly acknowledging geographic proximity‐based spatial dependencies in these patterns. At a methodological level, the paper focuses on specifying and estimating a spatial MDCP model that allows the dependent variable to exist in multiple discrete states with an intensity associated with each discrete state. The formulation also accommodates spatial dependencies, as well as spatial heterogeneity and heteroskedasticity, in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decision agents (or observation units) lead to spillover effects in multiple discrete‐continuous choices (or states). A simulation exercise is undertaken to evaluate the ability of the proposed maximum approximate composite marginal likelihood (MACML) approach to recover parameters from a cross‐sectional spatial MDCP model. The results show that the MACML approach does well in recovering parameters. An empirical demonstration of the approach is undertaken using the city of Austin parcel level land use data.     

Estimating Rapidity of Change in Complex Urban Systems: A Multidimensional,Local‐Scale Approach

This study illustrates an exploratory approach based on a Multiway Factor Analysis (MFA) to estimate rapidity of change in complex urban systems, based on “fast” and “slow” variables. The proposed methodology was applied to 18 socioeconomic indicators of long‐term (1960–2010) transformations in 115 municipalities of Athens’ metropolitan area (Greece), including demography, land‐use/planning, and urban form and functions. Athens was regarded as a dynamic urban area with diversified structures and functions at the local scale, expanding through a self‐organized pattern rather than a centralized planning strategy. Athens’ urban system was described using nine supplementary (topographic and territorial) variables and 30 independent indicators assessing the local context in recent times. Exploratory data analysis found an increasing connectedness and redundancy among socioeconomic indicators during the phase of largest urban expansion (1960–1990). Only the rate of population growth was classified as a “fast” variable for all five decades investigated. The overall rapidity of change was higher in 1960–1970, 1980–1990, and 2000–2010, decades that coincided with specific phases of urban expansion driven by migration inflow, second‐home suburbanization, and Olympic games, respectively. Rapidity of change was high for functional indicators during all five decades studied, while demography indicators changed more rapidly in the first three decades and land‐use/planning indicators in the last two decades. Rapidity of change was highest in peri‐urban municipalities with a highly diversified economic structure dominated by industry. Our methodology provides a comprehensive overview of the transformations of a complex urban system, quantifying low‐level indicators that are rarely assessed in the mainstream literature on urban studies. These results may contribute to design policies addressing complexity and promoting resilience in expanding metropolitan areas.     

The p‐Regions Problem. p‐区域问题

The p‐regions problem involves the aggregation or clustering of n small areas into p spatially contiguous regions while optimizing some criteria. The main objective of this article is to explore possible avenues for formulating this problem as a mixed integer‐programming (MIP) problem. The critical issue in formulating this problem is to ensure that each region is a spatially contiguous cluster of small areas. We introduce three MIP models for solving the p regions problem. Each model minimizes the sum of dissimilarities between all pairs of areas within each region while guaranteeing contiguity. Three strategies designed to ensure contiguity are presented: (1) an adaptation of the Miller, Tucker, and Zemlin tour‐breaking constraints developed for the traveling salesman problem; (2) the use of ordered‐area assignment variables based upon an extension of an approach by Cova and Church for the geographical site design problem; and (3) the use of flow constraints based upon an extension of work by Shirabe. We test the efficacy of each formulation as well as specify a strategy to reduce overall problem size.     

NEIGHBORHOOD DYNAMICS AND PRICE EFFECTS OF SUPERFUND SITE CLEAN‐UP*

ABSTRACT Numerous hedonic price analyses estimate price effects associated with hazardous waste site remediation or other environmental variation. This paper estimates a neighborhood transition model to capture the direct price effect from Superfund site clean‐up and the indirect price effects arising from residential sorting and changes in investment in the housing stock following clean‐up. First‐difference models of neighborhood change and a national sample are used. This approach fails to find consistent positive direct price effects. Positive indirect effects, however, may arise through residential sorting and neighborhood investment spurred by remediation. The findings can be sensitive to policy endogeneity and model specification.     

国内外城市居住空间研究的回顾与展望

住宅作为城市的重要职能和城市空间结构的重要组成部分,长期以来地理学、经济学、社会学、政治学等不同学科的学者一直关注城市居住和相关理论的研究。本文在全面阐述西方居住空间研究的理论、方法、研究领域和研究成果的基础上,对我国有关城市居住空间的研究领域和研究进展进行回顾,并展望有待进一步深化研究的领域。     

基于最优插值函数优选的小城镇商业用地地价空间结构分析——以西安市蓝田县城区为例

根据在西安市蓝田县城区调查的商业用地地价资料,利用ArcGis软件,从8种常用的插值方法中,以估计值与观察值的标准差最小为原则,筛选出适合于商业用地建立GRID数据的最优插值函数为克里金指数半变差函数模型,并利用该模型建立了商业用地的地价面,在此基础上对商业用地的地价空间结构进行了分析。结果表明:小城镇商业用地地价的变化呈同心圆结构,即城市中心区地价最高,向外逐渐降低。商业用地地价与道路两侧的商店密度性、临街道路的类型以及文化娱乐设施的分布密度正相关,相关系数分别为0.946、0.693和0.659;而与金融保险机构的个数以及土地所有权的性质相关性不强。     

A Zero‐One Programming Model for Contiguous Land Acquisition

The land acquisition problem is a spatial partitioning problem that involves selecting multiple parcels to be acquired for a particular land use. Three selection criteria are considered: total cost, total area, and spatial contiguity. Achieving contiguity or connectivity has been problematic in previous exact methods for land acquisition. Here we present a new zero‐one programming model that enforces necessary and sufficient conditions for achieving contiguity in discrete cell landscapes, independent of other spatial attributes such as compactness. Computational experience with several demonstration problems is reported, and results and extensions are discussed.     

LAND USE IN THE PRESENCE OF AN ATMOSPHERE EXTERNALITY,WITH AND WITHOUT CORRECTIVE TAXES*

ABSTRACT. Total cleared area in a von Thünen land-use model drives an atmosphere externality which depresses agricultural productivity uniformly throughout the region. Exogenous events that encourage clearance and use of a larger cultivated area (output price or population increase) exacerbate the externality. Imposition of a simple, corrective tax on land rents does not reverse these patterns but does mitigate the increase in the externality and leaves cultivators with higher incomes than they would obtain without the tax. We examine an optimal tax on land rents, designed to maximize the social value of land rents in the region, and an output tax.     

A SPATIO‐TEMPORAL KERNEL METHOD FOR MAPPING CHANGES IN PREHISTORIC LAND‐USE PATTERNS

Archaeologists are accustomed to considering both the spatial distributions of sites and the temporal distributions of dates as means of analysing the dynamics of prehistoric societies. However, spatial and temporal approaches have thus far remained largely separate, rather than being combined within a single, unified framework. A formal methodology is outlined that combines univariate kernel density estimation based on radiocarbon dates with bivariate kernel density estimation based on spatial site coordinates; the approach allows archaeologists to arrive at reconstructed land‐use distributions through time that not only correct for the problematic issue of site contemporaneity, but also reflect the continuous nature of the archaeological record. The model is implemented using as a data set a series of sites from the Mesolithic of Atlantic Iberia; the results demonstrate that it is capable of providing key insights into changing patterns of land use that are not apparent from either the temporal or the spatial perspective alone.     

我国西部城镇土地定级估价的特点——以西宁市为例

土地定级与估价是城市地产评估的两个方面,都是对城市土地利用适宜性和土地价格的评定,是以城市土地区位和土地使用效益的集聚程度及空间分布规律为依据。在城市土地定级估价研究中,根据城市特点选定不同的方法和因素及指标体系,对不同类型和特点的城镇,其因素的重要性和区位差异性有着较大的差别。因此在定级估价中,首先要针对城镇的特点进行分析,从而确定其方法路线和有关因素指标体系,才能得到客观理想的评估结果。本文以西宁市土地定级估价为例,对我国西部城镇土地定级估价特点和方法进行了分析。     

A Theory‐Based Cellular Automata for the Simulation of Land‐Use Change. 基于理论细胞自动机的土地利用变化模拟

This study proposes a theory‐based cellular automata (CA) in which CA rules are constructed on the basis of a theory of urban land‐use change and other local conditions. Rent gap theory that describes urban redevelopment and exogenous conditions that reflect urban development are embedded into a CA to simulate changes of land use. The theoretical framework provides a strong conceptual background for the CA simulation. A case study demonstrates the flexibility of the integrated framework for simulating land‐use changes in complex settings. This research aims to help decision‐makers formulate appropriate development plans.     

Dynamics of Land Surface Temperature in Response to Land‐Use/Cover Change

In this study, we employed Geographical Information Systems and remote sensing techniques to investigate the impact of land‐use/cover change on land surface temperature (LST) in a rapidly urbanisation city, Kunming in south‐west China. Spatial patterns of LST and land use for 1992 and 2006 were derived from Landsat images to examine how LST responded to urban growth. Remote sensing indices were used to quantify land‐use types and employed as explanatory variables in LST modelling. The geographically weighted regression (GWR), a location dependent model, was performed to explore the influences of the spatially varied land‐use conditions on the LST patterns. Results revealed that rapid urbanisation in Kunming altered the local thermal environment, particularly in increasing the LST in the zone surrounding the urban core. Remote sensing indices demonstrated that water and vegetation played an important role in mitigating the urban heat island effect, while built‐up and barren land accounted for the increase in LST. The GWR improved the goodness‐of‐fit for LST modelling and provided insights into the spatially varied relationship between LST and land‐use conditions.     

Bilad al‐Qadim revisited: recent archaeological research at the Al‐Khamis Mosque,Ain Abu Zaydan and Abu Anbra,Bahrain

In comparison to our understanding of pre‐Islamic occupation and land use on Bahrain, that of the Islamic period has remained less well known. In connection with the building of a visitor centre at the Al‐Khamis Mosque and the planning of an associated heritage trail, renewed archaeological research has taken place in Bilad al‐Qadim, an archaeologically important area of Islamic settlement in the north‐east of the main island of Awal. This has involved excavations in the Abu Anbra cemetery, at Ain Abu Zaydan and at the Al‐Khamis Mosque. The results of these excavations are reported here and these contribute to our understanding of Islamic settlement on Bahrain by supporting the interpretation that Bilad al‐Qadim was the main centre of Islamic settlement in the eleventh–thirteen century AD.     

A Geostatistical Framework for Area‐to‐Point Spatial Interpolation

The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data and the sought‐after point predictions. In particular, it is proved that appropriate modeling of all area‐to‐area and area‐to‐point covariances required by the geostatistical frame‐work yields coherent (mass‐preserving or pycnophylactic) predictions. In other words, the areal average (or areal total) of point predictions within any arbitrary area informed by an areal‐average (or areal‐total) datum is equal to that particular datum. In addition, the proposed geostatistical framework offers the unique advantage of providing a measure of the reliability (standard error) of each point prediction. It is also demonstrated that several existing approaches for area‐to‐point interpolation can be viewed within this geostatistical framework. More precisely, it is shown that (i) the choropleth map case corresponds to the geostatistical solution under the assumption of spatial independence at the point support level; (ii) several forms of kernel smoothing can be regarded as alternative (albeit sometimes incoherent) implementations of the geostatistical approach; and (iii) Tobler's smooth pycnophylactic interpolation, on a quasi‐infinite domain without non‐negativity constraints, corresponds to the geostatistical solution when the semivariogram model adopted at the point support level is identified to the free‐space Green's functions (linear in 1‐D or logarithmic in 2‐D) of Poisson's partial differential equation. In lieu of a formal case study, several 1‐D examples are given to illustrate pertinent concepts.     

Patterns and Determinants of Historical Woodland Clearing in Central‐Western New South Wales,Australia

We consider the history of woodland clearing in central western New South Wales, Australia, which has led to the present highly cleared and fragmented landscape. A combined approach is used examining available historical land‐use data and using regression analysis to relate the pattern of cleared and wooded areas in the recent landscape to environmental variables, taking into account the contagious nature of clearing. We also ask whether it would be possible to apply a simple simulation modelling approach to reconstruct a credible historical sequence of clearing in the study area. The historical data indicate that annual clearing rates have varied substantially in the study area and selective tree removal (ringbarking and thinning) has been common. These findings make it unlikely that a simple simulation approach would replicate the spatial and temporal sequence of woodland loss. Our regression results show that clearing patterns can be related to environmental variables, particularly annual rainfall and estimated pre‐European vegetation type, but that patterns are dominated by contagion.