Generating Alternative Land‐use Allocation for Mixed Use Areas: Multi‐Objective Optimization Approach

Current research is carried out with an intention to present an optimization approach for the urban land‐use allocation problem by generating Pareto optimum solutions considering two objectives—maximizing compatibility among adjacent space uses of a study area without compromising the area’s total land price and maximizing the price of plot of each individual owner. Considering the non‐linear characteristics of the objective functions, a multi‐objective evolutionary algorithm approach called Non‐Dominated Sorting Genetic Algorithm‐II (NSGA‐II) is applied to obtain Pareto optimal land‐use allocation subject to different set of constraints. The objective functions are tested over a case study area of Dhaka, Bangladesh. The resulting NSGA‐II model produces 24 Pareto optimal solutions of land‐use allocation, allowing tradeoff between maximizing compatibility and land price from one solution to other. This research also expresses the potential of the model to aid the policymakers and city planners of development authorities by providing alternative land‐use plans, and thereby predicting the consequences of any plan before practical application.     

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.     

Spatial Modelling in Decision Support for Land‐Use Planning: a Demonstration from the Lal Lal Catchment,Victoria, Australia

The outcomes of selected, contested development permit appraisals in a Victorian water catchment show that implications regarding the development status of other land parcels were not examined during negotiation and/or appeal. Given access to an adequate spatial data base, Geographical Information System (GIS) technology can be used to model such implications rapidly enough to be useful in decision support, thereby significantly widening the common ground that can be sought while seeking resolution to land‐use planning conflicts.     

Comparison of GIS‐Based Logic Scoring of Preference and Multicriteria Evaluation Methods: Urban Land Use Suitability

Multi‐criteria evaluation (MCE) methods are useful tools to evaluate the land suitability for various uses and assist in the effective management of available land. Many common GIS‐based MCE methods, such as analytical hierarchy process (AHP), ordered weighted averaging (OWA), and a combination of AHP and OWA methods (AHP–OWA) are not able to fully represent all the logic that constitute a wide range of human decision‐making reasoning. Consequently, improved GIS‐based MCE methods such as Logic Scoring of Preference (LSP) method are needed. The main objectives of this study are to: (1) implement the GIS‐based LSP method for land suitability evaluation and (2) compare qualitatively and quantitatively the suitability maps generated by LSP and three GIS‐based MCE methods. This study was implemented with data sets from Boulder County, Colorado, USA for the case study of the urban land suitability evaluation. The qualitative properties of MCE methods and the Receiver Operating Characteristic (ROC) statistics were used as comparison metrics. The results indicate that soft computing methods and particularly LSP performed the best among GIS‐based MCE methods for the urban land use application.     

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.     

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.     

Incorporating Soil Associations into Linear Programming Models for Development of Irrigation Scenarios

An inadequate streamflow supply in Saginaw Bay, Michigan, affects the irrigation development in the region. Linear programming (LP) models are developed in this paper to help irrigation planners determine optimal irrigation scenarios. To incorporate the spatial variations of the soil and climate into the optimization models, soil associations are used as the basic analysis unit for estimating crop yields, irrigation requirements, and expected economic returns. The yields and irrigation requirements of corn, drybeans, and soybeans are simulated by the CERES-MAIZE, BEANGRO, and SOYGRO models at each of the twelve soil associations in the study area over the period of 1951–1980. The yield and irrigation requirement of sugarbeets are simulated by the YIELD model for all the soils in the study area over the period of 1956–1987. The crop prices, simulated crop yields and irrigation requirements, and variable costs are used to compute the expected gross margins and other coefficients in the LP models. The optimization results provide irrigation scenarios that are linked to specific soil associations, which are more useful to decision makers for identifying and targeting individual soils for irrigation development than are the spatially lumped optimal solutions. This study demonstrates that through integration of GIS and simulation models, spatial variations of the climate, soil, and water resources can be better represented in the optimization models to produce more realistic irrigation scenarios.     

“Power to the people”: Contesting urban poverty and power inequities through open GIS

Geospatial technologies are central to spatial decision making and governance, but gaining equitable access to these is still difficult for traditionally marginalized communities. We contend that the dominance of proprietary GIS software has contributed to this digital divide, as these are inherently disempowering to marginalized social groups. Their high purchasing cost and licensing fees pose access barriers to resource‐poor citizens. Design of proprietary software may also not be appropriate for marginalized groups who are neither trained in GIS, nor represent the needs of dominant market base. Therefore, “free and open source software for geospatial” (FOSS4G) and open GIS provide new opportunities in democratizing GIS, as these are open code and free of purchasing and licensing costs. This paper aims to discuss the role of open GIS in advancing the goals of public participation GIS (PPGIS). We first discuss the origins of the FOSS movement, and explore the ways it has shaped the FOSS4G and open GIS movements. Next, we examine how a community information system built with open GIS software is being successfully utilized by an environmental organization in Milwaukee, to contest urban poverty. Our research demonstrates that open source GIS offers unique opportunities in advancing PPGIS research and practice.     

ECONOMIC FUNDAMENTALS AND URBAN–SUBURBAN DISPARITIES

ABSTRACT This paper develops a spatially explicit model to examine how urban and suburban communities evolve differently with changes in local economic fundamentals such as rising income or falling commuting costs in the metropolitan area. The model highlights the importance of environmental amenities and the economy of scale in the provision of public services as determinants of urban spatial structure. Results suggest that urban sprawl, income segregation, and jurisdictional disparities are driven by the same economic conditions and thus tend to co‐exist. Rising incomes or falling commuting costs for high‐income households in a metropolitan area tend to increase land prices and public services in every community, while rising incomes or falling commuting costs for low‐income households can have the opposite effects.     

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.     

Modeling Air Quality in Urban Areas: A Cell‐Based Statistical Approach

Statistical regression models are presented that explain the observed variations, across urban areas, in the concentrations of two major pollutants, ozone and carbon monoxide. Model specification and estimation are based on an explicit and new spatial framework derived from the theoretical concept of well‐mixed cells, whereby the basic Fickian system of diffusion equations is integrated over the regional space partitioned into a grid of large cells. The concentration in each cell results from the balance of pollutant flows into and out of this cell and of pollutant emissions and removal within that cell, and is expressed as the sum of two concentration contributions: (1) the local effect, dependent upon pollution‐related factors around the measuring station, and (2) the regional effect, dependent upon pollutant flows originating outside the local area. A large database is developed, making extensive use of GIS technology, to spatially relate such data as pollution measurements, meteorological factors, land‐use characteristics, census socioeconomic data, and major highway network characteristics. The results confirm the appropriateness of the well‐mixed cell framework, are in line with general knowledge regarding the determinants of ozone and carbon monoxide concentrations, and clarify the role of transportation, residential fuel use, economic activities, natural environments, and meteorological factors such as temperature and solar radiation. About SO percent of the variations in concentrations are explained by these models. Several areas of further research are outlined.     

Contiguity Constraints for Single‐Region Site Search Problems

This paper proposes an explicit set of constraints as a general approach to the contiguity problem in site search modeling. Site search models address the challenging problem of identifying the best area in a study region for a particular land use, given that there are no candidate sites. Criteria that commonly arise in a search include a site's area, suitability, cost, shape, and proximity to surrounding geographic features. An unsolved problem in this modeling arena is the identification of a general set of mathematical programming constraints that can guarantee a contiguous solution (site) for any 0–1 integer‐programming site search formulation. The constraints proposed herein address this problem, and we evaluate their efficacy and efficiency in the context of a regular and irregular tessellation of geographic space. An especially efficient constraint form is derived from a more general form and similarly evaluated. The results demonstrate that the proposed constraints represent a viable, general approach to the contiguity problem.     

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.     

Doing Independent Overseas Fieldwork 2: Getting Funding

The development and testing of a Web‐based GIS e‐learning resource is described. This focuses on the application of GIS for siting a nuclear waste disposal facility and the associated principles of spatial decision‐making using Boolean and weighted overlay methods. Initial student experiences in using the system are analysed as part of a research project on teaching GIS concepts to large numbers of students with little or no prior GIS experience. Some general thoughts on the utility of Web‐based GIS for learning and teaching are presented. Results from the first cohort of 167 undergraduate/postgraduate geography students using the system indicate that students find it easy to use, a useful aid to learning about the issues involved, and a thought‐provoking exercise in Internet‐based democracy.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

Multi‐Type,Multi‐Zone Facility Location

The placement of facilities according to spatial and/or geographic requirements is a popular problem within the domain of location science. Objectives that are typically considered in this class of problems include dispersion, median, center, and covering objectives—and are generally defined in terms of distance or service‐related criteria. With few exceptions, the existing models in the literature for these problems only accommodate one type of facility. Furthermore, the literature on these problems does not allow for the possibility of multiple placement zones within which facilities may be placed. Due to the unique placement requirements of different facility types—such as suitable terrain that may be considered for placement and specific placement objectives for each facility type—it is expected that different suitable placement zones for each facility type, or groups of facility types, may differ. In this article, we introduce a novel mathematical treatment for multi‐type, multi‐zone facility location problems. We derive multi‐type, multi‐zone extensions to the classical integer‐linear programming formulations involving dispersion, centering and maximal covering. The complexity of these formulations leads us to follow a heuristic solution approach, for which a novel multi‐type, multi‐zone variation of the non‐dominated sorting genetic algorithm‐II algorithm is proposed and employed to solve practical examples of multi‐type, multi‐zone facility location problems.     

A Spatial Price Determination Model for Multi‐Market Heterogeneously Distributed Resources: An Application to Forestry Markets in Sweden

The spatial dimension is a key paradigm in price determination, as attested by recent studies in the literature that highlighted the differential in market behavior between spatial and non‐spatial pricing settings. In this paper, we develop a model of spatial pricing for multi‐market heterogeneously distributed resources, with an application to the Swedish forestry sector. The focus of the model is to estimate the impact of spatial interaction on the demand for resources in terms of resource allocation, competition, and pricing. In its core, the pricing mechanism relies on a supply–demand framework. Using disaggregated data at the gridcell level for forest feedstock supply and harvesting costs in Sweden, we construct regional supply curves for each gridcell assuming a maximum transportation distance to delimit the potential market. Demand nodes are exogenously determined and are adjusted using a distance‐decay model to assess demand pressure across locations. We apply the model empirically to assess the impact on forest feedstock prices of a 20 TWh increase in biofuel production.     

Geographic information system‐based edge effect correction for Ripley's K‐function under irregular boundaries

Ripley's K‐function is a test to detect geographically distributed patterns occurring across spatial scales. Initially, it assumed infinitely continuous planar space, but in reality, any geographic distribution occurs in a bounded region. Hence, the edge problem must be solved in the application of Ripley's K‐function. Traditionally, three basic edge correction methods were designed for regular study plots because of simplified geometric computation: the Ripley circumference, buffer zone, and toroidal methods. For an irregular‐shaped study region, a geographic information system (GIS) is needed to support geometric calculation of complex shapes. The Ripley circumference method was originally implemented by Haase and has been modified into a Python program in a GIS environment via Monte Carlo simulation (hereafter, the Ripley–Haase and Ripley–GIS methods). The results show that in terms of the statistical powers of clustering detection for irregular boundaries, the Ripley–GIS method is the most stable, followed by the buffer zone, toroidal, and Ripley–Haase methods. After edge effects of irregular boundaries have been eliminated, Ripley's K‐function is used to estimate the degree of spatial clustering of cities in a given territory, and in this paper, we demonstrate that by reference to the relationship between urban spatial structure and economic growth in China.     

Facilitating native land reacquisition in the rural USA through collaborative research and Geographic Information Systems

A large proportion of American Indian reservation lands are owned by non‐Indian entities. A Geographic Information System (GIS) is a powerful tool for visualising land tenure changes, and public participation GIS (PPGIS) is one approach for using spatial technologies to facilitate the identification and reacquisition of reservation lands by tribes. While some tribes have successfully harnessed GIS for land management and for systematically identifying lands for reacquisition, others struggle to implement land management systems such as GIS for these purposes. This paper situates PPGIS in relation to other forms of participatory action research and outlines our use of a PPGIS framework to engage undergraduate geography students in the mapping of land tenure status on ten rural Minnesota Indian reservations as part of a collaborative partnership with the Indian Land Tenure Foundation (ILTF). A PPGIS framework allowed us to collaboratively define research goals in response to tribal community needs and provided structure for student work with partner reservations to develop and implement tailored mapping and analysis techniques. Two sets of findings are significant. First, the assembly of a standardised set of maps for American Indian reservations in Minnesota provides a tremendous visual and analytical resource for ILTF and individual tribes to pursue land reacquisition within reservation boundaries. Second, from a PPGIS perspective, we found that working with a coordinating or ‘bridging’ organisation provided key benefits by enabling education of both the student–faculty partners and the individual tribes. The PPGIS model empowered both partners by allowing tribes to harness a powerful technology to assist in visualising land‐based assets and allowing students to contribute to native land reacquisition efforts through application of their GIS skills. This mapping helps facilitate economic and cultural viability in tribal communities by providing an important visual catalogue of existing land‐based assets, in support of future land acquisition and economic development planning.