Geocomputation and Urban Planning

Sixteen years ago, Franklin (1992) estimated that about 80% of data contain geo-referenced information.

To date, the availability of geographic data and information is growing, together with the capacity of users to operate with IT tools and instruments. Spatial data infrastructures are growing and allow a wide number of users to rely on them. This growth has not been fully coupled to an increase of knowledge to support spatial decisions.

Modern urban planning needs efficient descriptors of the distribution of socio-economic features in space and time. Knowledge about residential patterns and distribution of services can help the decision makers for future strategies of cities’ development. In order to facilitate this process, clustering of urban features is a very efficient tool, because it allows the reduction of the information from a very high-dimensional and complex input space to a low dimensional and visualizable output space. In this chapter, an unsupervised clustering method and a cluster detection method are discussed and applied to analyze the socio-economic structure of the Swiss regions of Vaud and Geneva. The unsupervised method, based on self-organized maps and hierarchical ascending classification, groups the spatial units by their similarity measured between socio-economic variables. The self-organizing map allows to account for nonlinear similarity. The cluster detection method, the spatial scan statistics, is used to find hot spots in the distribution of the residential patterns of professions. The method is applied to the distribution of business manager and workers in the region of Vaud. Moreover, the distribution of hotels and restaurant services has been studied at the intra-urban scale, to detect over- and under-densities of services and compare them to the residential patterns observed previously. Results show the effect of peri- and sub-urbanization in the region and are discussed in both transportation and social terms.

The paper is focused on the observation of urban form and functions and is aimed at identifying a method for the cartographic definition and representation of CBD (Central Business District). The analysis is developed to explore the formation of centers of different order in the urban environment, starting from the locations of a selected set of human activities located in urban areas. An index of concentration of central activities is presented to allow the visualization of the functional urban environment by means of a density surface, therefore highlighting areas where central activities and functions concentrate. The paper is based on analyses related to spatial statistics in a GIS environment. We provide a short review of the literature on CBD research, briefly describe the kernel density estimation method, and propose how this can be used in order to test the index of concentration of activities and therefore delineating CBD, presenting evidence from two urban areas in Northeastern Italy (Trieste and Udine).

The objective of the present work is to use statistical data to identify territorial zones characterized by the presence of urban poverty related to property ownership and the availability of residential services. Poverty clusters have a high concentration of poor people, but that does not mean that everyone living in them is poor. While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved very difficult to develop measures that both capture this multidimensionality and make comparisons over time and space easy. With this in mind, we attempt to apply a Total Fuzzy and Relative (TFR) approach, based on a fuzzy measure of the degree of association of an individual to the totality of the poor and an approach of semantic distance (Munda 1995), based on the definition of a “fuzzy distance” as a discriminating multidimensional reference to rank the availability to property in real estate market, as complement of urban poverty, in the specific case of the City of Bari. These approaches have been improved using the SaTScan methodology, a circle-based spatial-scan statistical method (Kulldorff 1997; Patil and Taille 2004; Aldstat and Getis 2006). It concerns geoinformatic surveillance for poverty hot-spot detection, used as a scientific base to lead urban regeneration policies.

The paper is concerned with a thematic issue, that is the segregation of social housing in urban peripheries, and with a methodological discussion, regarding the configurational approach to the analysis of urban settlements. Different techniques based on such method are here applied on social housing areas located in the edge of towns, in order to account for the effects of the configuration of the urban grid on their condition of segregation and marginality. The purpose is to test the configurational techniques on several case studies, so as to prove them as a reliable and useful tool to analyse and understand such areas, but, even more, to support town planning both in the project of new housing estates and in redeveloping and rehabilitating the diseased ones.

In this paper analysis of a transportation system in emergency conditions due to an hazardous events is considered. To assess the effects on the transport network analysed, extension to a mesoscopic dynamic traffic assignment (DTA) model was developed in order to determine quantitative indicators for estimating the exposure component of total risk incurred by the transport networks in an area. In particular, the ability to allow for multi-modal (user) flows and network reliability was introduced. To give a practical example of the proposed model, it has been applied to a real case, studying evacuation in the hypothesis that in the event of a calamity, population in the area follows the instructions proposed by the municipal civil protection plan. The work shows that adequate quantitative methodologies based on a dynamic approach can be a useful tool to support the process of evacuation planning at several regional scales.

This chapter aims at identifying accident hot spots by means of a local indicator of spatial association (LISA), more in particular Moran’s I. A straightforward use of this LISA is impossible, since it is not tailor-made for applications in traffic safety. First of all, road accidents occur on a network, so Moran’s I needs to be adapted to account for this. Moreover, its regular distributional properties are not valid under the circumstances of Poisson distributed count data, as is the case for accidents. Therefore, a Monte Carlo simulation procedure is set up to determine the correct distribution of the indicator under study, though this can be generalized to any kind of LISA. Moran’s I will be adapted in such a way, that it can overcome all the previously stated problems. Results are presented on highways in a province in Flanders and in a city environment. They indicate that an incorrect use of the underlying distribution would lead to false results. Next to this, the impact of the weight function is thoroughly investigated and compared in both settings. The obtained results may have a large impact for policy makers, as money could be allocated in a completely wrong way when an unadjusted LISA is used.

Nearly half a century has passed since Lynch described visual quality of American cities. Although the issue of visual impact assessment in urban planning is not new, only few experiences exist considering visual aspect when realizing new development zones. Visual aspects are fundamental in urban planning, since each plan choice can generate manipulation or obstruction of urban elements, producing negative effects on the image of the city. Viewshed analysis can help to achieve a more objective and consequently more effective analysis of visual impacts. Traditional viewshed analyses (single, multiple and cumulative) do not show which target is visible from a certain cell. On this purpose, a new viewshed analysis has been developed, the Identifying Viewshed, which shows how many and which objects are visible in several areas. The implemented extension has been tested in three different contexts, Laurenzana and Venosa, in Basilicata Region, and Pisa, in Tuscany.

The mesoscale meteorological models are the most used to study air quality and pollutant dispersion processes in urban areas. However they do not have the spatial resolution to directly simulate the fluid dynamics and thermodynamics in and around buildings and other urban structures that can modify the atmospheric characteristics. In order to improve the quality and consistency of mesoscale models the most extensively adopted approach is the "Urban Canopy Parametrization" (UCP) which allows to describe geometric and morphological characteristics of urban agglomerations by a range of parameters derived from analysis of high resolution databases. This work has the aim to analytically determine some of these parameters and an automatic procedure was implemented by Arcgis 9, using as input data the vectorial numerical geodatabase of the city of Rome, coded in 1: 2.000 scale and provided by CARTESIA S.p.A. This procedure was applied to the IX district of the city of Rome, whose full extent is about 8.1 km².

The sustainable management of geographic information through time is fundamental in the field of spatial planning, because handling of long-term statistical indicators is useful for setting up prospective scenarios. Sustainability means the control of complex information which is often incomplete: data come from multiple scales and heterogeneous grids, from various suppliers, at many dates, with a semantic which keeps evolving. Under these conditions, a framework for data quality assessment and estimation of missing values is necessary. This paper draws out a proposition for a system based on an object oriented spatio-temporal data model fit to figure out some relationships of interest when managing missing values, and providing a support for storage and exploitation of metadata through the usage of thematic and geographic ontologies.

In this paper we present an application of GIS and Airborne Light Detection and Ranging (LIDAR) data analysis for computation of buildings visibility, solar exposition and extraction of morphological indicators in the context of urban group analysis and morphogenesis geosimulation. The interdisciplinary project between geomaticians and architects aims to provide a simulation model based upon dynamic computation of building agents’ satisfaction degree, incorporating analysis results using a hybrid approach derived from GIS and raw LIDAR data.

This paper analyses the spatial characterization of urban expansion by using spatial fractal analysis applied to multidate Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) satellite imagery. The investigation was focused on four small towns in southern Italy, for which the border was extracted from NASA Landsat images acquired in 1976 (MSS), in 1991 (TM) and 1999 (ETM). The border was analyzed using the box counting method, which is a well-know technique to estimate the spatial fractal dimension, that quantifies the shape irregularity of an object. The obtained results show that the fractal dimension of the border of the investigated towns is a good indicator of the dynamics of the regular/irregular urban expansion.

This paper proposes a new modelling approach which deals with the spatial spreading of built-up areas. The aim is twofold: first, to predict the broad outlines of a built-up areas extension on a regional scale, secondly, to provide decision makers with a tool which allows them to explore spatial consequences of different urbanization policies. Our spatial modelling is similar to cellular automata but differs in the use of image processing method and mathematical morphology algorithms. In this model, the spatial spread process depends on both proximity and morphology of the built-up areas located in a space whose configuration also determines the shape of the spatial spread. These features explain the "spatio morphological" qualifier given to this model. The distinctive features of the both methods will be discussed before focusing the attention on the spatio-morphological modelling and detailing the stages of the approach. The model will be then applied to simulate the built-up areas spread on a coastal region of the Southern France.

Urban planning is confronted with multifaceted complexities, related to the complex nature of phenomena, dynamics and processes it has to deal with. We argue that good tools for planning must be informed by these complexities, and therefore must have specific characteristics, in terms of modularity, flexibility, user-friendliness, generality, adaptability, computational efficiency and cost-effectiveness. In this chapter we present and try to make the case for a multi-agent geosimulation infrastructure framework called MAGI, showing how it delivers as such a tool for planning. The modelling and simulation infrastructure MAGI possesses characteristics, features and computational strategies particularly relevant for strongly geo-spatially oriented agent-based simulations. The infrastructure is composed of a development environment for building and executing simulation models, and a class library based on open source components. Differently from most of the existing tools for geosimulation, both raster and vector representation of simulated entities are allowed and managed with efficiency. This is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agents.

The current research deals with the development of a Multi Agent System able to analyse and simulate the dynamics of urban system sustainable mobility as derived from millions of choices performed by the individuals belonging to the system itself. In this research an Activity Based Model is devised in order to reconstruct urban mobility. With the objective to perform a dynamic decision support system for the comprehension of phenomena influencing mobility at urban scale, this research tries to gather in the model both the territorial features, by the construction of a detailed spatial database able to contain all the services the city offers, and the direct extraction of citizens behavioural rules from survey data by Artificial Intelligence tools. To this end the population sample questionnaire structuring, the “temporal” geodatabase construction and the agents behavioural rules extraction, via two different Data Mining techniques, represent the most relevant innovations experimented till now.