António M. Rodrigues

Sensitivity Analysis of Spatial Autocorrelation Using Distinct Geometrical Settings: Guidelines for the Quantitative Geographer

Inferences based on spatial analysis of areal data depend greatly on the method used to quantify the degree of proximity between spatial units - regions. These proximity measures are normally organized in the form of weights matrices, which are used to obtain statistics that take into account neighbourhood relations between agents. In any scientific field where the focus is on human behaviour, areal datasets are greatly relevant since this is the most common form of data collection (normally as count data). The method or schema used to divide a continuous spatial surface into sets of discrete units influences inferences about geographical and social phenomena, mainly because these units are neither homogeneous nor regular. This article tests the effect of different geometrical data aggregation schemas - administrative regions and hexagonal surface tessellation - on global spatial autocorrelation statistics. Two geographical variables are taken into account: scale (resolution) and form (regularity). This is achieved through the use of different aggregation levels and geometrical schemas. Five different datasets are used, all representing the distribution of resident population aggregated for two study areas, with the objective of consistently test the effect of different spatial aggregation schemas.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

The use of multivariate micro-data, data aggregated for small-areas, allows detailed analysis of the physical and social structures of regional landscapes. Such exercises are in many cases univariate and static in nature; this happens when geometries are not coincident between datasets. Common solutions to such inconsistencies involve the use of areal interpolation techniques to build coherent information sets; when ancillary information is available, dasymetric mapping using control units may then be used. Techniques vary on the type and quality of the ancillary (or control) information. The purpose of the present article is to present a generalized tool to tackle common practical analytical problems and which produces geometrically coherent datasets. It is generalised because: (1) it is flexible, allowing distinct parametrization depending on the data; (2) it is based on Open Source tools anchored on robust database management systems (DBMS) technologies. Its aim is to provide the regular GIS user with a tool to tackle a common problem of geometric mismatch.

Assessing Patterns of Urban Transmutation Through 3D Geographical Modelling and Using Historical Micro-Datasets

The increasing volume of empty houses in historical cities constitute a challenge in times of economic crisis and acute housing needs. In order build coherent guidelines and implement effective policies, it is necessary to understand long-term patterns in city growth. The present work analyses urban dynamics at the micro level and present clues concerning transmutation in Lisbon, Portugal, using 3D geographical modelling to estimate potential housing supply. The recent availability of detailed demographic historical micro-datasets presents an opportunity to understand long-term trends.

Comuns: An Open-Data Provider, Explorer and Analytic Toolbox Based on FOSS

Researh efforts in the Social Sciences have a great potential impact on society in general as it promotes greater knowledge of any and every aspect of human interactions. With this in mind, a combined project - Comuns, was developed, bringing together recent technological trends in data exploration and visualization, and the production of new high-precision historical geo-demographic datasets. Using dasymetric algorithms implemented within an Free and Open Source Software (FOSS) platform, source geographical data- aggregated according to distinct geometrical schema, was re-allocated according to common areas. The innovative nature of Comuns is three-folded: (1) for the first time, a large scale comprehensive time-space database is built for Portuguese census data; (2) the fact that data is made available online contributes to the goal of making knowledge symmetrically available; (3) the fact the only FOSS is used means that, other than man-hours, the project is costs’ free, accountability increases as does reproducibility. The open character of the project have potential implications in terms of the way human landscapes are perceived, given that new coherent datasets allow new explorations of the territory. Its target are primarily civil society in general; its applications range from academic to recreational, with potential uses in geomarketing projects.

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Presently, the use of new technologies for the acquisition of 3D geographical data on time is very important for urban planning. Applications include evaluation and monitoring of urban parameters (ie. volumetric data),indicators of an urban plan, or monitoring built-up areas and illegal buildings. This type of 3D data can be acquired through an Airborne Laser Scanning system, also known as LiDAR (Light Detection And Ranging) or by Unmanned Aerial Vehicles (UAV). The aim of this article is to use and compare these two technologies for extracting building parameters (facade height and volume). Existing literature evaluates each technology separately. This work pioneers benchmarking between LiDAR and UAV point-clouds. The basic function of LiDAR is collecting a georeferenced and dense 3D point-cloud from a laser scanner during flight. Therefore it is possible to obtain a similar 3D point-cloud using processing algorithms for stereo aerial images, obtained by large or small-format digital cameras (the small-format camera implemented in Unmanned Aerial Vehicles). The chosen study area is located in Praia de Faro, an open sandy beach in Algarve (Southern Portugal), limited west by the Ria Formosa barrier island system. The area defined has an extension of 300100m. The methodology is divided in two distinct stages: (1) building parameters extraction, (2) comparative technology analysis. Lidar point-cloud resolution is approximately 6 pts/m2 and UAV point-cloud 60 pts/m2. FOSS technologies have proven to be the most adequate adequate platform for the development and diffusion of advanced analytical tools in the Geographical Information Sciences (GISci). Data management in this paper is supported by a Geographical Database Management System (GDBMS), implemented using PostgreSQL and PostGIS. Statistical analysis is performed using R whilst advanced spatial functions are used in GRASS.

Sensitivity Analysis of Spatial Autocorrelation Using Distinct Geometrical Settings: Guidelines for the Urban Econometrician

Inferences based on spatial analysis of areal data depend greatly on the method used to quantify the degree of proximity between spatial units - regions. These proximity measures are normally organized in the form of weights matrices, which are used to obtain statistics that take into account neighbourhood relations between agents. In any scientific field where the focus is on human behaviour, areal datasets are immensely relevant since this is the most common form of data collection (normally as count data). The method or schema used to divide a continuous spatial surface into sets of discrete units influence inferences about geographical and social phenomena, mainly because these units are neither homogeneous nor regular. This article tests the effect of different geometrical data aggregation schemas on global spatial autocorrelation statistics. Two geographical variables are taken into account: scale (resolution) and form (regularity). This is achieved through the use of different aggregation levels and geometrical schemas. Five different datasets are used, all representing the distribution of resident population aggregated for two study areas, with the objective of consistently test the effect of different spatial aggregation schemas.