Inés Santé

High performance genetic algorithm for land use planning

Abstract This study uses genetic algorithms to formulate and develop land use plans. The restrictions to be imposed and the variables to be optimized are selected based on current local and national legal rules and experts’ criteria. Other considerations can easily be incorporated in this approach. Two optimization criteria are applied: land suitability and the shape-regularity of the resulting land use patches. We consider the existing plots as the minimum units for land use allocation. As the number of affected plots can be large, the algorithm execution time is potentially high. The work thus focuses on implementing and analyzing different parallel paradigms: multi-core parallelism, cluster parallelism and the combination of both. Some tests were performed that show the suitability of genetic algorithms to land use planning problems.

A comparative analysis of cellular automata models for simulation of small urban areas in Galicia, NW Spain

Abstract Urban growth models developed in the second half of the 20th century have allowed for a better understanding of the dynamics of urban growth. Among these models, cellular automata (CA) have become particularly relevant because of their ability to reproduce complex spatial and temporal dynamics at a global scale using local and simple rules. In the last three decades, many urban CA models that proved useful in the simulation of urban growth in large cities have been implemented. This paper analyzes the ability of some of the main urban CA models to simulate growth in a study area with different characteristics from those in which these models have been commonly applied, such as slow and low urban growth. The comparison of simulation results has allowed us to analyze the strengths and weaknesses of each model and to identify the models that are best suited to the characteristics of the study area. Results suggest that models which simulate several land uses can capture better land use dynamics in the study area but need more objective and reliable calibration methods.

An analysis of the effect of the stochastic component of urban cellular automata models

Urban cellular automata models have proved useful tools in urban growth prediction because of their simplicity and their ability to reproduce complex emergent dynamics. Complex emergent dynamic systems involve processes that are difficult to predict, in which randomness plays a key role. In view of the fact that randomness is particularly relevant to complex processes, the aim of this paper is to analyze the sensitivity of the results of urban cellular automata models to the different methods used to incorporate the stochastic component in the models. The urban growth patterns obtained using different stochastic components are analyzed and compared using a number of spatial metrics. The results show that the differences observed in the simulated patterns are sufficiently relevant to justify the need for this type of analysis, which allows for the selection of the stochastic component that best suits the dynamics of the area.

Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain

Cellular automata (CA) stand out among the most commonly used urban models for the simulation and analysis of urban growth because of their ability to reproduce complex dynamics, similar to those found in real cities, from simple rules. However, CA models still have to overcome some shortcomings related to their flexibility and difficult calibration. This study combines various techniques to calibrate an urban CA that is based on one of the most widely used urban CA models. First, the number of calibration parameters is reduced by using various statistical techniques, and, second, the calibration procedure is automated through a genetic algorithm. The resulting model has been assessed by simulating the urban growth of Ribadeo, a small village of NW Spain, characterized by low, slow urban growth, which makes the identification of urban dynamics and consequently the calibration of the model more difficult. Simulation results have shown that, by automating the calibration procedure, the model can be more easily applied and adapted to urban areas with different characteristics and dynamics. In addition, the simulations obtained with the proposed model show better values of cell-to-cell correspondence between simulated and real maps, and the values for most spatial metrics are closer to real ones.

A population-based iterated greedy algorithm for the delimitation and zoning of rural settlements

Abstract In this paper we present a Population-Based Iterated Greedy (PBIG) algorithm for delimiting and zoning rural settlements. Each cadastral plots is allocated to a category (traditional–historical, common or none) considering restrictions such as the characteristics of the existing edifications and the building density. Since the problem has multiple solutions, heuristic search algorithms, as PBIG, are a good strategy to solve it. Besides the resolution of the problem according to the requirements of the laws, our work explores also new methods of delimitation. The comparison between both types of solutions can help to improve the current methodology. The algorithm, implemented using the Java programming language and integrated into an open-source GIS software, has been tested in rural settlements with different morphological characteristics, providing adjustable solutions to the specific needs of each rural settlement.

Classification of rural landscapes from low-density lidar data: is it theoretically possible?

Lidar technology has become an important data source in 3D terrain modelling. In Spain, the National Plan for Aerial Orthophotography will soon release public low-density lidar data (0.5–1 pulses/m2) for most of the country territory. Taking advantage of this fact, this article experimentally assesses the possibility of classifying a rural landscape into eight classes using multitemporal and multidensity lidar data and analyses the effect of point density on classification accuracy. Two statistical methods (transformed divergence and the Jeffries–Matusita distance) were used to assess the possibility of discriminating the eight classes and to determine which data layers were best suited for classification purposes. The results showed that ‘dirt road’ cannot be discriminated from ‘bare earth’ and that the possibility of discriminating ‘bare earth’, ‘pavement’, and ‘low vegetation’ decreases when using densities below 4 pulses/m2. Two non-parametric tests, the Kruskal–Wallis test and the Friedman test, were used to strengthen the results by assessing their statistical significance. According to the results of the Kruskal–Wallis test, lidar point density does not significantly affect the classification, whereas the results of the Friedman test show that bands could be considered as the only parameter affecting the possibility of discriminating some of the classes, such as ‘high vegetation’. Finally, the J48 algorithm was used to perform cross-validation in order to obtain the most familiar quantitative values in the international literature (e.g. overall accuracy). Mean overall accuracy was around 85% when the eight classes were considered and increased up to 95% when ‘dirt road’ was disregarded.

Web-GIS tool for the management of rural land markets: Application to the Land Bank of Galicia (NWSpain)

Land abandonment and stagnation of rural markets in the last few years have become one of the main concerns of rural administrations. The use of Web and GIS (Geographic Information System) technologies can help to mitigate the effects of these problems. This paper pro-poses a novel Web-GIS tool with spatial capabilities for the dynamization of rural land markets by encouraging the transfer of land from owners to farmers through the leasing of plots. The system, based on open source software, offers information about the properties, their environment and their owners. It uses standards for handling the geographic information and for communicating with external data sources. This system was used as the basis for the development of SITEGAL, the tool for the management of the Land Bank of Galicia (www.bantegal.com/sitegal). SITEGAL has been operational since 2007 obtaining benefits for both administration and users (farmers and land owners), and promoting the e-Government.

A simulated annealing algorithm for zoning in planning using parallel computing

Abstract There is an increasing demand for tools that support land use planning processes, particularly the design of zoning maps, which is one of the most complex tasks in the field. In this task, different land use categories need to be allocated according to multiple criteria. The problem can be formalized in terms of a multiobjective problem. This paper generalizes and complements a previous work on this topic. It presents an algorithm based on a simulated annealing heuristic that optimizes the delimitation of land use categories on a cadastral parcel map according to suitability and compactness criteria. The relative importance of both criteria can be adapted to any particular case. Despite its high computational cost, the use of plot polygons was decided because it is realistic in terms of technical application and land use laws. Due to the computational costs of our proposal, parallel implementations are required, and several approaches for shared memory systems such as multicores are analysed in this paper. Results on a real case study conducted in the Spanish municipality of Guitiriz show that the parallel algorithm based on simulated annealing is a feasible method to design alternative zoning maps. Comparisons with results from experts are reported, and they show a high similarity. Results from our strategy outperform those by experts in terms of suitability and compactness. The parallel version of the code produces good results in terms of speed-up, which is crucial for taking advantage of the architecture of current multicore processors.

An open source GIS-based Planning Support System: Application to the land use plan of La Troncal, Ecuador

Planning Support Systems (PSS) comprise a wide variety of geo-technological tools related to GIS and spatial modeling aimed at addressing land planning processes. This article describes the OpenRules system, a PSS based on a previous system called RULES. Among OpenRules new features are its architecture, based exclusively on free and open source software, and its applicability to all land use types, including rural and urban uses. In addition, OpenRules incorporates an unlimited number of land evaluation factors and a new objective in land use spatial allocation. OpenRules has been programmed in Java and implemented as a module of the free GIS software gvSIG, with full integration between the GIS and the decision support tools. Decision support tools include multicriteria evaluation, multiobjective linear programming and heuristic techniques, which support three basic stages of land use planning processes, namely land suitability evaluation, land use area optimization and land use spatial allocation. The application of OpenRules to the region of La Troncal, Ecuador, demonstrates its capability to generate alternative and coherent solutions through a scientific and justified procedure at low cost in terms of time and resources.

Land-Development Dynamics by Morphological Areas: A Case Study of Ribadeo, Northwest Spain

The need to understand land-development processes in order to address the problem derived from urban growth led to the implementation of a number of scientific methods aimed at explaining urban-growth patterns. The dynamics that originate these patterns are complex and may vary across space, such that in small areas there may be various processes operating and producing different kinds of growth. Our aim is to use cluster-analysis techniques to identify zones with similar urban-growth patterns in a coastal rural municipality of the northwest of Spain. Then, the processes that originated the different growth patterns identified from the cluster analysis are characterized using logistic regression techniques. The methodology differentiated three clusters (an urban cluster, a rural cluster, and a rural cluster with urban influence) and characterized the underlying dynamics. This proves that the techniques used in this study constitute a straightforward tool to identify and analyze areas with uniform land-development patterns in order to gain deeper knowledge and produce better regulations and zoning for each area.