Segmentación geométrica de la ciudad de Quito por medio de un método de aprendizaje no supervisado

This document contains an application of data analysis methods on urbanism and Geographic Information Systems. It aims to find shape and density patterns in the map of Quito, starting from the assumption that there are some similarities between the city blocks in terms of its geometric features and its spatial location. The Hierarchical Agglomerative and DBSCAN algorithms have been considered as analysis tools. Since the result is a new partition of the city, in which the new neighborhoods or sectors are not defined in an arbitrary way, we open the door for rethinking urban planning.El presente documento recoge una aplicación de métodos de análisis de datos en urbanismo y Sistemas de Información Geográfica. Se apunta a encontrar patrones de forma y densidad en el mapa de Quito, partiendo del supuesto de que existen similitudes entre las manzanas de la urbe en términos de sus atributos geométricos y su ubicación espacial. Los algoritmos Jerárquico Aglomerativo y DBSCAN han sido considerados como herramientas de análisis. Siendo el resultado una nueva segmentación de la ciudad, en la cual los nuevos barrios o sectores no están definidos de manera arbitraria, se abre la puerta a un replanteamiento de la planificación urbana

Redistricting using Heuristic-Based Polygonal Clustering

Redistricting is the process of dividing a geographic area into districts or zones. This process has been considered in the past as a problem that is computationally too complex for an automated system to be developed that can produce unbiased plans. In this paper we present a novel method for redistricting a geographic area using a heuristic-based approach for polygonal spatial clustering. While clustering geospatial polygons several complex issues need to be addressed – such as: removing order dependency, clustering all polygons assuming no outliers, and strategically utilizing domain knowledge to guide the clustering process. In order to address these special needs, we have developed the Constrained Polygonal Spatial Clustering (CPSC) algorithm that holistically integrates domain knowledge in the form of cluster-level and instance-level constraints and uses heuristic functions to grow clusters. In order to illustrate the usefulness of our algorithm we have applied it to the problem of formation of unbiased congressional districts. Furthermore, we compare and contrast our algorithm with two other approaches proposed in the literature for redistricting, namely – graph partitioning and simulated annealing

Improving the geospatial consistency of digital libraries metadata

Consistency is an essential aspect of the quality of metadata. Inconsistent metadata records are harmful: given a themed query, the set of retrieved metadata records would contain descriptions of unrelated or irrelevant resources, and may even not contain some resources considered obvious. This is even worse when the description of the location is inconsistent. Inconsistent spatial descriptions may yield invisible or hidden geographical resources that cannot be retrieved by means of spatially themed queries. Therefore, ensuring spatial consistency should be a primary goal when reusing, sharing and developing georeferenced digital collections. We present a methodology able to detect geospatial inconsistencies in metadata collections based on the combination of spatial ranking, reverse geocoding, geographic knowledge organization systems and information-retrieval techniques. This methodology has been applied to a collection of metadata records describing maps and atlases belonging to the Library of Congress. The proposed approach was able to automatically identify inconsistent metadata records (870 out of 10,575) and propose fixes to most of them (91.5%) These results support the ability of the proposed methodology to assess the impact of spatial inconsistency in the retrievability and visibility of metadata records and improve their spatial consistency

Geometric Approximations and their Application to Motion Planning

Geometric approximation methods are a preferred solution to handle complexities (such as a large volume or complex features such as concavities) in geometric objects or environments containing them. Complexities often pose a computational bottleneck for applications such as motion planning. Exact resolution of these complexities might introduce other complexities such as unmanageable number of components. Hence, approximation methods provide a way to handle these complexities in a manageable state by trading off some accuracy. In this dissertation, two novel geometric approximation methods are studied: aggregation hierarchy and shape primitive skeleton. The aggregation hierarchy is a hierarchical clustering of polygonal or polyhedral objects. The shape primitive skeleton provides an approximation of bounded space as a skeleton of shape primitives. These methods are further applied to improve the performance of motion planning applications. We evaluate the methods in environments with 2D and 3D objects. The aggregation hierarchy groups nearby objects into individual objects. The hierarchy is created by varying the distance threshold that determines which objects are nearby. This creates levels of detail of the environment. The hierarchy of the obstacle space is then used to create a decom-position of the complementary space (i.e, free space) into a set of sampling regions to improve the efficiency and accuracy of the sampling operation of the sampling based motion planners. Our results show that the method can improve the efficiency (10 − 70% of planning time) of sampling based motion planning algorithms. The shape primitive skeleton inscribes a set of shape primitives (e.g., sphere, boxes) inside a bounded space such that they represent the skeleton or the connectivity of the space. We apply the shape primitive skeletons of the free space and obstacle space in motion planning problems to improve the collision detection operation. Our results also show the use of shape primitive skeleton in both spaces improves the performance of collision detectors (by 20 − 70% of collision detection time) used in motion planning algorithms. In summary, this dissertation evaluates how geometric approximation methods can be applied to improve the performance of motion planning methods, especially, sampling based motion planning method

SOLAP+

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática,como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaNo início do século XXI, Bédard propôs incorporar ao modelo multi-dimensional dados geográficos, originando o conceito SOLAP. Através deste conceito, os analistas puderam obter melhores análises das estruturas e relações de dados espaciais, mantendo as características benéficas que provêm dos sistemas OLAP, isto é, informação sumarizada, análise de dados a diferentes níveis de granularidade, exploração interactiva dos dados, etc. Devido à adequação dos sistemas SOLAP, face aos sistemas OLAP para o processo de suporte à decisão, algumas aplicações têm sido desenvolvidas. Porém, estas aplicações têm sido concebidas para um contexto específico. Com o propósito de se transpor esta limitação, vários foram os trabalhos que culminaram num modelo genérico SOLAP. Apesar de este modelo solucionar diversas limitações de aplicações anteriores, actualmente, não suporta análises com duas entidades espaciais em simultâneo, por exemplo “total de voos entre o aeroporto para o aeroporto ” ou “qual a quantidade de resíduos lançados por uma indústria no rio ?”. Por outro lado, a visualização dos mapas pode despoletar o efeito contrário ao desejado. Facilmente o mapa poderá ficar desorganizado devido ao excesso de objectos geográficos presentes, o que prejudica a visualização/análise de dados espaciais. Para dar resposta a estas questões, esta dissertação pretende estender o modelo SOLAP genérico, de modo a suportar análises onde estão presentes duas entidades espaciais em simultâneo e integrar algoritmos de agrupamento espacial, com o objectivo de garantir a visibilidade do mapa em situações de excesso de objectos geográficos