A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

Improving Nearest Neighbour Search in 3D Spatial Access Method

Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized

Data driven tools to assess the location of photovoltaic facilities in urban areas

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.This work has been supported by the project RTI2018-095097-B-I00 at the 2018 call for I+D+i Project of the Ministerio de Ciencia, Innovación y Universidades, Spain. Funding for open access charge: Universidad de Málaga/CBUA, Spain

Integrated Framework for Inclusive Town Planning Using Fuzzy Analytic Hierarchy Method for a Semi Urban Town

Planning is a continuous process and must incorporate a regular evaluation of implementation and further revision for effective and efficient utility for the betterment of society through modification of the planning standards. Development plans for cities / towns are criticized for being rigid and static, having little regard for investment planning efforts, and taking a very long time in the process of formulation and approval. In depth analysis and review of the existing situation, covering the demographic, economic, financial, infrastructure, physical, environmental, and institutional aspects, is important so as to identify the strengths and weaknesses in the city overall development. In the present study, an attempt has been made to thoroughly review the existing planning standards adopted for the preparation and implementation of development plans in India, especially in Maharashtra. Since the development plan's objectives are not measurable, this study will use the Fuzzy Analytic Hierarchy Process (AHP) to assess their level of performance. For the purpose of identifying the various viewpoints of various stakeholders, field surveys and questionnaire surveys were conducted. This application can be used as an objective evaluation tool for planners and policy makers to improve planning practices and provide necessary knowledge for revising plans. The results indicated the importance of criteria from the pre-planning, preparation, and implementation stages of DP. These results were used for two semi-urban towns in Maharashtra regions and could also be used by planning engineers for further development of planning standards. Doi: 10.28991/CEJ-2022-08-12-07 Full Text: PD

Reflecting Human Knowledge of Place and Route-Choice Behavior Using Big Data

Exploring human knowledge of geographical space and related behavior not only helps in understanding human-environment interactions and dynamic geographic processes, but also advances Geographic Information Systems (GIS) toward a human-centric paradigm to make daily life more efficient. Today’s relatively easy acquisition of various big data provides an unprecedented opportunity for geographers to answer research questions that previously could not be adequately addressed. However, new challenges also arise regarding data quality and bias as well as change in methodology for dealing with big data that are different from traditional data types. Representing people’s perception of place and studying driver’s route-choice behavior are two of the many applications of big data in answering research questions about human knowledge and behavior in the fields of GIS and transportation. Incorporating three papers, this dissertation focuses on these two different applications to achieve the following objectives: 1) examine the degree to which a geographic place’s spatial extent can be estimated from human-generated geotagged photos; 2) address the challenge of geotagged photos’ uneven spatial distribution in place estimation and explore an approach that can better derive a place’s spatial extent; 3) develop a method that can properly estimate the spatial extent of a place that has multiple disjoint regions while considering geotagged photos’ uneven distribution; 4) explore useful spatiotemporal patterns of taxi drivers’ route-choice behavior in a dynamic urban environment. This dissertation makes three major contributions to big data applications’ systematic theory: 1) proposes an effective approach to handling the uneven spatial distribution problem of geotagged photos as a type of volunteered geographic data by modeling their representativeness; 2) develops methods that can properly derive the vague spatial extent of a place with or without disjoint regions; and 3) explores taxi drivers’ route-choice patterns in different situations that can inform future transportation decisions and policy-making processes

Machine Learning based Mountainous Skyline Detection and Visual Geo-Localization

With the ubiquitous availability of geo-tagged imagery and increased computational power, geo-localization has captured a lot of attention from researchers in computer vision and image retrieval communities. Significant progress has been made in urban environments with stable man-made structures and geo-referenced street imagery of frequently visited tourist attractions. However, geo-localization of natural/mountain scenes is more challenging due to changed vegetations, lighting, seasonal changes and lack of geo-tagged imagery. Conventional approaches for mountain/natural geo-localization mostly rely on mountain peaks and valley information, visible skylines and ridges etc. Skyline (boundary segmenting sky and non-sky regions) has been established to be a robust natural feature for mountainous images, which can be matched with the synthetic skylines generated from publicly available terrain maps such as Digital Elevation Models (DEMs). Skyline or visible horizon finds further applications in various other contexts e.g. smooth navigation of Unmanned Aerial Vehicles (UAVs)/Micro Aerial Vehicles (MAVs), port security, ship detection and outdoor robot/vehicle localization.\parProminent methods for skyline/horizon detection are based on non-realistic assumptions and rely on mere edge detection and/or linear line fitting using Hough transform. We investigate the use of supervised machine learning for skyline detection. Specifically we propose two novel machine learning based methods, one relying on edge detection and classification while other solely based on classification. Given a query image, an edge or classification map is first built and converted into a multi-stage graph problem. Dynamic programming is then used to find a shortest path which conforms to the detected skyline in the given image. For the first method, we provide a detailed quantitative analysis for various texture features (Scale Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and their combinations) used to train a Support Vector Machine (SVM) classifier and different choices (binary edges, classified edge score, gradient score and their combinations) for the nodal costs for Dynamic Programming (DP). For the second method, we investigate the use of dense classification maps for horizon line detection. We use Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as our classifier choices and use normalized intensity patches as features. Both proposed formulations are compared with a prominent edge based method on two different data sets.\par We propose a fusion strategy which boosts the performance of the edge-less approach using edge information. The fusion approach, which has been tested on an additional challenging data set, outperforms each of the two methods alone. Further, we demonstrate the capability of our formulations to detect absence of horizon boundary and detection of partial horizon lines. This could be of great value in applications where a confidence measure of the detection is necessary e.g. localization of planetary rovers/robots. In an extended work, we compare our edge-less skyline detection approach against deep learning networks recently proposed for semantic segmentation on an additional data set. Specifically, we compare our proposed fusion formulation with Fully Convolutional Network (FCN), SegNet and another classical supervised learning based method.\par We further propose a visual geo-localization pipeline based on evolutionary computing; where Particle Swarm Optimization (PSO) is adopted to find/refine an orientation estimate by minimizing the cost function based on horizon-ness probability of pixels. The dense classification score image resulting from our edge-less/fusion approach is used as a fitness measure to guide the particles toward best solution where the rendered horizon from DEM perfectly aligns with the actual horizon from the image without even requiring its explicit detection. The effectiveness of the proposed geo-localization pipeline is evaluated on a decent sized data set

Mobility in Lisbon based on smartphone data

This research covers five months (September, October, November, December 2021, and January 2022) of georeferenced data of the Vodafone mobile phone service, provided by the municipality of Lisbon (CML). The motivation of this research regards the fact that the urban mobility study with mobile phone data is a relatively unexplored topic. This study focused on the city of Lisbon, with a case study conducted in the parish of Santa Maria Maior with the aim to understand the urban mobility patterns of mobile phone users. The number of roaming and non-roaming devices in the case study is related to the subject of a vibrant neighborhood and tourism, characterized by transportation and historical points of interest. We used a data mining approach to analyze mobility trends, adopting a CRISP-DM methodology, to perform statistical analysis, visualization, and clustering (DBSCAN) methods. Results showed eight clusters in Santa Maria Maior, with outstanding clusters along 28-E electric tram and Lisbon Cruise Terminal. Foremost, we looked at these two clusters and performed a forecast model with Prophet, resulting in downward trend, influenced by the pandemic restrictions in December and January data. This thesis contributes considerably to the digital transformation of Lisbon into a smart city by understanding urban mobility patterns with smartphone data of no roaming and roaming users.Este estudo abrange cinco meses (setembro, outubro, novembro, dezembro de 2021 e janeiro de 2022) de dados georreferenciados do serviço da operadora móvel Vodafone, fornecido pela Câmara Municipal de Lisboa (CML). A motivação da tese considera o facto de o estudo da mobilidade urbana com dados de telemóveis ser um tópico relativamente inexplorado. Este estudo centrou-se na cidade de Lisboa, com um caso de estudo na freguesia de Santa Maria Maior com o objetivo de compreender os padrões de mobilidade urbana dos utilizadores da rede móvel. O número de dispositivos de nãoroaming e roaming no caso de estudo está relacionado com o tema das ‘vibrant neighborhoods’ e turismo, caracterizado por pontos de interesse históricos e de transportes. Utilizámos uma abordagem de ‘data mining’ para analisar as tendências de mobilidade, adotando uma metodologia CRISP-DM, para realizar análise estatística, visualização e agrupamentos (DBSCAN). Os resultados mostraram nove agrupamentos em Santa Maria Maior, dos quais dois agrupamentos de destaque, um ao longo do elétrico 28-E e outro à volta do Terminal de Cruzeiros de Lisboa. Em primeiro lugar, analisámos estes dois agrupamentos e realizámos análises de previsão, resultando numa tendência decrescente, como consequência das restrições da pandemia nos meses de dezembro e janeiro. Esta tese contribui consideravelmente para a transformação digital de Lisboa numa cidade inteligente, ao compreender os padrões de mobilidade urbana com dados dos utilizadores da rede móvel em não-roaming e roaming

Patterns of mobility in a smart city

Transportation data in smart cities is becoming increasingly available. This data allows building meaningful, intelligent solutions for city residents and city management authorities, the so-called Intelligent Transportation Systems. Our research focused on Lisbon mobility data, provided by Lisbon municipality. The main research objective was to address mobility problems, interdependence, and cascading effects solutions for the city of Lisbon. We developed a data-driven approach based on historical data with a strong focus on visualization methods and dashboard creation. Also, we applied a method based on time series to do prediction based on the traffic congestion data provided. A CRISP-DM approach was applied, integrating different data sources, using Python. Hence, understand traffic patterns, and help the city authorities in the decision-making process, namely more preparedness, adaptability, responsiveness to events.Os dados de transporte, no âmbito das cidades inteligentes, estão cada vez mais disponíveis. Estes dados permitem a construção de soluções inteligentes com impacto significativo na vida dos residentes e nos mecanismos das autoridades de gestão da cidade, os chamados Sistemas de Transporte Inteligentes. A nossa investigação incidiu sobre os dados de mobilidade urbana da cidade de Lisboa, disponibilizados pelo município. O principal objetivo da pesquisa foi abordar os problemas de mobilidade, interdependência e soluções de efeitos em cascata para a cidade de Lisboa. Para alcançar este objetivo foi desenvolvida uma metodologia baseada nos dados históricos do transito no centro urbano da cidade e principais acessos, com uma forte componente de visualização. Foi também aplicado um método baseado em series temporais para fazer a previsão das ocorrências de transito na cidade de Lisboa. Foi aplicada uma abordagem CRISP-DM, integrando diferentes fontes de dados, utilizando Python. Esta tese tem como objetivo identificar padrões de mobilidade urbana com análise e visualização de dados, de forma a auxiliar as autoridades municipais no processo de tomada de decisão, nomeadamente estar mais preparada, adaptada e responsiva

A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB