Abstraction and cartographic generalization of geographic user-generated content: use-case motivated investigations for mobile users

On a daily basis, a conventional internet user queries different internet services (available on different platforms) to gather information and make decisions. In most cases, knowingly or not, this user consumes data that has been generated by other internet users about his/her topic of interest (e.g. an ideal holiday destination with a family traveling by a van for 10 days). Commercial service providers, such as search engines, travel booking websites, video-on-demand providers, food takeaway mobile apps and the like, have found it useful to rely on the data provided by other users who have commonalities with the querying user. Examples of commonalities are demography, location, interests, internet address, etc. This process has been in practice for more than a decade and helps the service providers to tailor their results based on the collective experience of the contributors. There has been also interest in the different research communities (including GIScience) to analyze and understand the data generated by internet users. The research focus of this thesis is on finding answers for real-world problems in which a user interacts with geographic information. The interactions can be in the form of exploration, querying, zooming and panning, to name but a few. We have aimed our research at investigating the potential of using geographic user-generated content to provide new ways of preparing and visualizing these data. Based on different scenarios that fulfill user needs, we have investigated the potential of finding new visual methods relevant to each scenario. The methods proposed are mainly based on pre-processing and analyzing data that has been offered by data providers (both commercial and non-profit organizations). But in all cases, the contribution of the data was done by ordinary internet users in an active way (compared to passive data collections done by sensors). The main contributions of this thesis are the proposals for new ways of abstracting geographic information based on user-generated content contributions. Addressing different use-case scenarios and based on different input parameters, data granularities and evidently geographic scales, we have provided proposals for contemporary users (with a focus on the users of location-based services, or LBS). The findings are based on different methods such as semantic analysis, density analysis and data enrichment. In the case of realization of the findings of this dissertation, LBS users will benefit from the findings by being able to explore large amounts of geographic information in more abstract and aggregated ways and get their results based on the contributions of other users. The research outcomes can be classified in the intersection between cartography, LBS and GIScience. Based on our first use case we have proposed the inclusion of an extended semantic measure directly in the classic map generalization process. In our second use case we have focused on simplifying geographic data depiction by reducing the amount of information using a density-triggered method. And finally, the third use case was focused on summarizing and visually representing relatively large amounts of information by depicting geographic objects matched to the salient topics emerged from the 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

Visual Analytics Methodologies on Causality Analysis

abstract: Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Interactive density maps for moving objects

Trajectories capture the movements of objects with multiple attributes. A visualization method called density maps shows trends in these trajectories. Density map creation involves aggregating smoothed trajectories in a density field and then visualizing the field. Users can explore attributes along trajectories by calculating a density field for multiple data subsets. The method then either combines these density fields into a new density field or visualizes them and then combines them. Using an interactive distribution map, users can define subsets and, supported by graphics hardware, get fast feedback for these computationally expensive density field calculations. Given the generic method and the lack of domain-specific assumptions, this method might also be applicable for trajectories in other domains