Placement of Infrastructure for Urban Electromobility: A Sustainable Approach

Over the last few years, electric vehicles (EVs) have turned into viable urban transportation alternatives. Charging infrastructure is an issue, since high investment is needed and there is a lot of demand uncertainty. Seeking to fill gaps in past studies, this investigation proposes a set of procedures to identify the most adequate places for implementing the EV charging infrastructure. In order to identify the most favorable districts for the installation and operation of electric charging infrastructure in São Paulo city, the following public available information was considered: the density of points of interest (POIs), distribution of the average monthly per capita income, and number of daily trips made by transportation mode. The current electric vehicle charging network and most important business corridors were additionally taken into account. The investigation shows that districts with the largest demand for charging stations are located in the central area, where the population also exhibits the highest purchasing power. The charging station location process can be applied to other cities, and it is possible to use additional variables to measure social inequality. Document type: Articl

tmap: Thematic Maps in R

Thematic maps show spatial distributions. The theme refers to the phenomena that is shown, which is often demographical, social, cultural, or economic. The best known thematic map type is the choropleth, in which regions are colored according to the distribution of a data variable. The R package tmap offers a coherent plotting system for thematic maps that is based on the layered grammar of graphics. Thematic maps are created by stacking layers, where per layer, data can be mapped to one or more aesthetics. It is also possible to generate small multiples. Thematic maps can be further embellished by configuring the map layout and by adding map attributes, such as a scale bar and a compass. Besides plotting thematic maps on the graphics device, they can also be made interactive as an HTML widget. In addition, the R package tmaptools contains several convenient functions for reading and processing spatial data

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Visual Analytics Methods for Exploring Geographically Networked Phenomena

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Investigating crime patterns in Egypt using crowdsourced data between 2011-2013

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCrime is a social phenomenon that negatively impinges upon the society on various levels. Such phenomena are ought to be measured and analyzed to achieve control over its presence and consequences. One of the ways for measurement and analysis involves the use of crime maps as vital tools for visualising crime related data. Getting access to crime data is undoubtedly a challenged endeavour faced by hurdles of data collection, storage and making it available for public access. In addition, coming up with useful relationships for extracting information and patterns for crime data analysis is a significant challenge as well. This research investigates the link between the spatial and temporal variables in crime related data collected from crowdsourcing. The research will capitalize on crime data gathered throughout the operation of an online project called Zabatak founded by the author since January 2011 in Egypt. The dataset consists of more than 2000 crime incidents from various geographical areas across Egypt. The research considers an exploratory analysis in trying to interpret crime patterns and trends. The results of this study have identified various interesting trends and patterns in the dataset. One of the major findings of this research points out a strong relationship between the spatial and temporal variables in Car-Theft incidents. In addition, It was possible in the study to relate crime types to the type of the geographical area. The research considers Spatio-Temporal analysis using Inhomogeneous Spatio-Temporal K-function and pair-correlation functions which have identified a Spatio-Temporal cluster and interaction in crime data which can open new ways for crime maps data analysis

Discovering spatio-temporal relationships a case study of risk modelling of domestic fires

A systematic risk analysis for mitigation purposes plays a crucial role in the context of emergency management in modern societies. It supports the planning of the general preparedness of the rescue forces and thus enhances public safety. This study applies the principles of knowledge discovery and data mining to support the development of a risk model for fire and rescue services. Domestic fires, which are a serious threat in an urban environment, are selected to demonstrate the methods. The aim of the research is to identify important factors that contribute to the probability of the occurrence of domestic fires. Various physical and socio-economic conditions in the background environment are analysed to provide an insight into the distribution of domestic fires in relation to underlying factors. Following the cross-disciplinary nature of data mining, this study offers a set of distinct methods that share the same goal - to identify patterns and relationships in data. The methods originate in different scientific fields, such as information visualisation, statistics, or artificial intelligence. Each of them reveals different aspects of the existing relations, which supports an understanding of the phenomenon and thus expands the expert knowledge. The application of data mining techniques is not straightforward because of the specific nature of geospatial data. This study documents the analysis process in order to provide guidelines for potential future users. It considers the suitability of the methods to handle spatial and spatio-temporal data with special attention to the GIS-motivated conceptualisation of the problem being analysed. Furthermore, the requirements for the user to be able to apply the methods successfully are discussed, as is the available software support

Use of HAND terrain descriptor for estimating flood-prone areas in river basins

O mapeamento de áreas inundáveis em uma bacia hidrográfica é fundamental para o gerenciamento do risco de inundações, estratégias mitigadoras e sistemas de previsão e alerta, entre outros benefícios. Uma abordagem para esse mapeamento é com base no descritor do terreno HAND (Height Above Nearest Drainage), derivado diretamente do Modelo Digital de Elevação (MDE), no qual cada pixel apresenta a diferença de elevação desse ponto em relação ao ponto da rede de drenagem ao qual ele se conecta. Considerando a bacia do rio Mamanguape (3.522,7 km²; Paraíba) como área de estudo, esta pesquisa adotou esse método e verificou sua aplicabilidade quanto a cinco aspectos: consideração de uma área mínima variável espacialmente para denotar o início da drenagem; impacto de considerar o MDE sem depressões; avaliação da condiçãohidrostática; efeito de incorporação de uma rede vetorial existente; análise comparativa à morfologia da bacia em termos do perfil longitudinal dos rios. Os resultados indicaram que adotar um valor uniforme de área mínima de contribuição para início da rede de drenagem é uma simplificação que deveria ser evitada, adotando-se a variação espacial de tal parâmetro, que influi no total e na distribuição espacial das áreas inundadas. Além disso, considerar o MDE sem depressões leva a maiores valores do HAND e menor área inundada (diferença variou de 3% a 99%), comparativamente ao MDE com depressões, embora apenas 3,1% dos pixels representem depressões. É recomendado considerar o MDE sem depressões, ao passo que o pré-processamento por incorporação de rede vetorial (stream burning) gera resultados incoerentes quanto à relação do HAND com o padrão morfológico representado no MDE. Concluiuse, ainda, que a estimativa de áreas inundáveis pelo HAND não garante a condição hidrostática, mas esse desacordo abrange uma região de extensão desprezível para fins práticos.The flood hazard mapping in a river basin is crucial for flooding risk management, mitigation strategies, and flood forecasting and warning systems, among other benefits. One approach for this mapping is based on the HAND (Height Above Nearest Drainage) terrain descriptor, directly derived from the Digital Elevation Model (DEM), in which each pixel represents the elevation difference of this point in relation to the river drainage network to which it is connected. Considering the Mamanguape river basin (3,522.7 km²; state of Paraíba, Brazil) as the study location, the present research applied this method and verified it as for five aspects: consideration of a spatially variable minimum drainage area for denoting the river drainage initiation; the impact of considering a depressionless DEM; evaluation of hydrostatic condition; effect of incorporating an existing river vector network; and comparative analysis of basin morphology regarding longitudinal river profiles. According to the results, adopting a uniform minimum drainage area for the river network initiation is a simplification that should be avoided, using a spatially variable approach, which influences the amount and spatial distribution of flooded areas. Additionally, considering the depressionless DEM leads to higher values of HAND and to a smaller flooded area (difference ranging between 3% and 99%), when compared with the use of DEM with depression, despite 3.1% of the pixels representing depressions. The use of the depressionless DEM is recommended, whereas the DEM pre-processing by incorporating a vector network (stream burning) generates dubious results regarding the relation between HAND and the morphological pattern presented in the DEM. Moreover, the estimation of flooded areas based on HAND does not guarantee the hydrostatic condition, but this disagreement comprises a negligible area for practical purposes

FOSS4G 2016 Proceedings: Academic Program - selected papers and posters

This Conference Proceedings is a collection of selected papers and posters submitted to the Academic Program of the International Conference for Free and Open Source Software for Geospatial (FOSS4G 2016), 24th to 26th August 2016 in Bonn, Germany. Like in previous FOSS4G conferences on national and international level the academic papers and posters cover an extensive wide range of topics reflecting the contribution of the academia to this field by the development of open source software components, in the design of open standards, in the proliferation of web-based solutions, in the dissemination of the open principles important in science and education, or in the collection and the hosting of freely available geo-data

Multi-Scale Flow Mapping And Spatiotemporal Analysis Of Origin-Destination Mobility Data

Data on spatial mobility have become increasingly available with the wide use of location-aware technologies such as GPS and smart phones. The analysis of movements is involved in a wide range of domains such as demography, migration, public health, urban study, transportation and biology. A movement data set consists of a set of moving objects, each having a sequence of sampled locations as the object moves across space. The locations (points) in different trajectories are usually sampled independently and trajectory data can become very big such as billions of geotagged tweets, mobile phone records, floating vehicles, millions of migrants, etc. Movement data can be analyzed to extract a variety of information such as point of interest or hot spots, flow patterns, community structure, and spatial interaction models. However, it remains a challenging problem to analyze and map large mobility data and understand its embedded complex patterns due to the massive connections, complex patterns and constrained map space to display. My research focuses on the development of scalable and effective computational and visualization approaches to help derive insights from big geographic mobility data, including both origin-destination (OD) data and trajectory data. Specifically, my research contribution has two components: (1) flow clustering and flow mapping of massive flow data, with applications in mapping billions of taxi trips (Chapter 2 and Chapter 3); and (2) time series analysis of mobility, with applications in urban event detection (Chapter 4). Flow map is the most common approach for visualizing spatial mobility data. However, a flow map quickly becomes illegible as the data size increases due to the massive intersections and overlapping flows in the limited map space. It remains a challenging research problem to construct flow maps for big mobility data, which demands new approaches for flow pattern extraction and cartographic generalization. I have developed new cartographic generalization approaches to flow mapping, which extract high-level flow patterns from big data through hierarchical flow clustering, kernel-based flow smoothing, and flow abstraction. My approaches represent a significant breakthrough that enables effective flow mapping of big data to discover complex patterns at multiple scales and present a holistic view of high-level information. The second area of my research focuses on the time series analysis of urban mobility data, such as taxi trips and geo-social media check-ins, to facilitate scientific understanding of urban dynamics and environments. I have developed new approaches to construct location-based time series from mobility data and decompose each mobility time series into three components, i.e. long-term trend, seasonal periodicity pattern and anomalies, from which urban events, land use types, and changes can be inferred. Specifically, I developed time series decomposition method for urban event detection, where an event is defined as a time series anomaly deviating significantly from its regular trend and periodicity