Paulo Morgado

A Bottom Up Approach to Modeling Habitat Connectivity Dynamics Through Networks Analysis

© 2012 Morgado et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A Bottom Up Approach to Modeling Habitat Connectivity Dynamics Through Networks Analysis

GIS Graph Tool for Modeling: Urban–Rural Relationships

As the world is becoming increasingly more urbanized and built-up areas (that have an artificial cover as a result of human activities such as: urban fabric, industrial, commercial and transport units, mine, dump and construction sites, sports and leisure facilities) are endangering functional diversity, several environmental and spatial planning problems arise, such as urban sprawl, city food safety, community vulnerability to climate change, coastal erosion, and pollution (air, water, and noise). Lately, urban–rural relationships have attracted a lot of attention from both national and international community governance, hoping to mitigate and combat some of these problems. Although the necessary steps are known, it is unclear how to address the problems. A good understanding of how actors relate to each other in urban–rural relationships is fundamental. This paper introduces a novel GIS graph-based model tool (Geo_ graph ) for mapping and measuring urban–rural relationships through an actor’s network. The GIS graph model tool contributes to a better understanding of the relationships being built over the years under government supervision, and it also shows the relationships’ resilience and how many of these relationships are vulnerable to setbacks. The GIS graph tool provides decision makers with a visual image and numeric indicators of the relative role played by each of the actors. The methodology is grounded in graph theory and it results in a data-driven form of modeling using preprocessed data in a GIS environment.

Modelling urban form: A multidimensional typology of urban occupation for spatial analysis:

The conceptual and methodological debate on urban form has grown in the last decades to recognize that social, economic, demographic and political processes can contribute to the development of new urban forms, especially those related to urban sprawl, as well as to find alternative methodologies for measuring them. Spatial metrics derived from landscape ecology arise as principal indicators to measure urban form. This paper proposes a typology of the urban occupation of Portuguese municipalities. It uses land use/cover data from 1990 and 2006 to extract built-up areas, and it presents five spatial metrics alongside seventeen statistical indicators from 1991 to 2011 most commonly used in the literature to characterize urban occupation. It uses a self-organising map as a visual tool to identify trends and relationships among variables and to cluster municipalities. Based on the self-organising map’s visual clustering, six types of urban occupation of Portuguese municipalities are proposed. In addition, the paper discusses the added value of using indicators that describe both the patterns and the characteristics of the municipalities for making spatial planning decisions in Portugal. The observed results show that spatial metrics are particularly adequate for measuring peri-urban municipalities (urban sprawl areas). These results represent the first multidimensional and systematic analysis of Portuguese urban occupation and they can be the first step in the integration of spatial metrics as indicators that are suitable for the analysis of spatial planning, and also for comparative purposes at a broader geographical scale.

Land use/cover classification through multiresolution segmentation and object oriented neural networks classification

In this paper is presented a land use/cover classification methodology of the rural/urban fringe, by means of the application of a neuronal network, with resource to the multiresolution image segmentation, construction of complex elements through object oriented analysis and integration of not spectral (ancillary) information. The study area is the municipality of Almada, located in the south bank of Tagus River and corresponding to one of the core regions of the Lisbon Metropolitan Area (Portugal). The data used was 2004 HRVIR SPOT images fused with supermode panchromatic image and the Portuguese urban quarter statistical data. The developed procedure is based in five steps: 1) Legend creation; 2) deriving statistical ancillary data; 3) deriving object (texture) data; 4) deriving spectral data and 5) neural network classification.