Sparse trajectory data are trajectories that cover a relatively large geographic area with infrequent samplings of movements, such as human migration and hurricane trajectories. Compared with dense trajectory data (e.g., massive vehicle movements in a metropolitan area), sparse trajectories represent a more general form of movement data due to the data availability and less sensitivity. However, there is a lack of open source software for sparse trajectory data analytics. We designed an online system with a self-adaptive heatmap and multi-spatial-temporal-view charts to analyze sparse trajectories both visually and interactively. With our system, users can (1) store and manage sparse trajectory data on a cloud service; (2) analyze sparse movement behaviors by querying consolidated and live data sets using keywords, spatial location, and time constraints; and (3) explore query results and associated data through web-based thematic maps, tables, and charts. Results of the survey among domain experts show that the open source system is intuitive and user-friendly.

Data containing geospatial semantics, such as geotagged tweets, travel blogs, and crime reports, associates natural language texts with geographical locations. This paper presents a lens-based visual interaction technique, GTMapLens, to flexibly browse the geo-text data on a map. It allows users to perform dynamic focus+context exploration by using movable lenses to browse geographical regions, find locations of interest, and perform comparative and drill-down studies. Geo-text data is visualized in a way that users can easily perceive the underlying geospatial semantics along with lens moving. Based on a requirement analysis with a cohort of multidisciplinary domain experts, a set of lens interaction techniques are developed including keywords control, path management, context visualization, and snapshot anchors. They allow users to achieve a guided and controllable exploration of geo-text data. A hierarchical data model enables the interactive lens operations by accelerated data retrieval from a geo-text database. Evaluation with real-world datasets is presented to show the usability and effectiveness of GTMapLens.

Videos embedded with spatial coordinates, especially when combined with additional expert insights, offer the potential to acquire fine-scale multi-time period contextualized data for a variety of different environments. However, while these geospatial multimedia (GSMM) data include abundant spatiotemporal, semantic and visual information, the means to fully leverage their potential using a suite of visual and interactive analysis techniques and tools has thus far been lacking. In this paper, we address this gap by first identifying the types of tasks required of GSMM data, and then presenting a solution platform. This GeoVisuals system utilizes a visual analysis approach built on semantic data points that can be integrated spatially, which in turn enables management in a unified database with combined spatio-temporal and text querying. A set of visualization functions are integrated in two investigation modes: geo-video analysis and geo-location analysis.

Community-level event (CLE) datasets, such as police reports of crime events, contain abundant semantic information of event situations and descriptions in a geospatial-temporal context. They are critical for frontline users, such as police officers and social workers, to discover and examine insights about community neighborhoods. We propose CLEVis, a neighborhood visual analytics system for CLE datasets, to help frontline users explore events for insights at community regions of interest (CROIs), namely fine-grained geographical resolutions such as small neighborhoods around local restaurants, churches, and schools. CLEVis fully utilizes semantic information by integrating automatic algorithms and interactive visualizations. The design and development of CLEVis are conducted with solid collaborations with real world community workers and social scientists. Case studies and user feedback are presented with real world datasets and applications.

We present an open source software, named as TrajAnalytics, which models, transforms, and then visualizes urban trajectory data for transportation and urban study. It allows researchers, administrations, and practitioners to understand the population mobility data and to discover knowledge intuitively. A conceptual data model is presented which integrates trajectory data with geo-structures through a variety of data access queries. The model guides us to develop data processing and management capability and support various visual queries through a Web-based visualization interface. A set of visualization widgets and interaction functions promote easy user engagement. A primary user study shows the usability of the software.

Accessibility of urban POIs (Points of Interest) is a key topic in a variety of urban sciences and applications as it reflects inherent city design, transportation, and population flow features. Isochrone maps and other techniques have been used to identify and display reachable regions from given POIs. In addition, domain experts further want to study the distribution effects of accessibility in the urban space such as finding spatial regions that have different accessibility patterns. Such patterns can be manifested by clustering POIs based on their accessibility of different time periods under different traffic conditions. In this paper, we present a visualization system that helps users to find and visualize Latent Accessibility Clusters (LACs) of POIs. The LACs discover temporally changing urban sub-regions (including nearby POIs) with disparate accessibilities at different times. LACs are computed by a POIGraph which connects POIs into a graph structure by extending the dual road network of the corresponding city. The LAC computation is facilitated by graph traversal over the POIGraph. By visualizing the LAC regions on the map, users can visually study the hidden patterns of spatial accessibility. It can contribute to urban transportation, planning, business, and related social sciences.

QuteVis uses multi-sketch query and visualization to discover specific times and days in history with specified joint traffic patterns at different city locations. Users can use touch input devices to define, edit, and modify multiple sketches on a city map. A set of visualizations and interactions are provided to help users browse and compare retrieved traffic situations and discover potential influential factors. QuteVis is built upon a transport database that integrates heterogeneous data sources with an optimized spatial indexing and weighted similarity computation. An evaluation with real-world data and domain experts demonstrates that QuteVis is useful in urban transportation applications in modern cities.

In modern cities, service providers want to identify regions hard to reach from multiple fire stations; a citizen wants to meet with friends in a restaurant close to each other; and administrators want to find whether an area far from two bus stations needs a new one. Such tasks involve studying the dynamic accessibility of urban structures over multiple geospatial and temporal constraints, which is an important topic in geographical sciences and urban transportation. In this paper, we present new computational model and a visualization system which help domain users to interactively study jointly-constrained accessible regions, street segments, and Points of Interest (POIs). In particular, USAVis system (Urban Structure Accessibility Visualization) is built upon a new Min-Max Joint Set (MinMaxJS) model, where specifically-designed set operations not only represent the accessible regions but also compute the minimum and maximum access times to urban structures from joint constraints. The computation and visualization are supported by a new graph model that accommodates the real-world dynamic traffic situation and the geographical settings of urban street segments and POIs. The visualization system allows users to conveniently construct and manage accessible regions, and visually explore urban structures inside them.

This paper presents a comparative study concerning the techniques of image fusion that aim at the improvement of the spatial resolution of multispectral satellite images for remote sensing applications. Three methods are used, Hue-Saturation-Intensity color transform (HSI), Principal Component Analysis (PCA), and Brovey technique. Two datasets of two satellites having different resolution and different resolution ratio are used, LANDSAT7 and IKONOS. The measurement of Trade-Off-Value, which is a measure of the degree of resolution improvement and spectral property preservation, is used to evaluate the given methods. The results have shown the superiority of the Brovey method for the LANDSAT7 dataset and the superiority of HSI for the IKONOS dataset.

Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as “What were the taxi trips starting from Main Street and ending at Wall Street in the morning?” or “Where are the taxis arriving at the Art Museum at noon typically coming from?”, tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as “Main Street”, “Wall Street”, and “Art Museum”. In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system.

We present a web-based software, named TrajAnalytics, for the visual analytics of urban trajectory datasets. It allows users to interactively visualize and analyze the massive taxi trajectories over urban spaces. The software offers data management capability and enable various visual queries through a web interface. A set of visualization widgets and interaction functions are developed to promote easy user engagement. We implemented two prototypes with realworld datasets: (1) the origin/destination (OD) data of taxi trips collected in New York City; and (2) the taxi trajectory data collected in Porto City in Portugal. This open source software supports practitioners, researchers, and decision-makers in advancing transportation and urban studies in the new era of smart city.

Thanks to advanced technologies in sensing and computing, the mobility patterns and dynamics of urban cities and their citizen are recorded and manifested in a variety of urban trajectory datasets, which include the moving paths of human, taxi, bus, fleets, cars, and so on. Understanding and analyzing such large-scale, complex data is of great importance to enhance both human lives and urban environments. Supported by National Science Foundation, TrajAnalytics aims to provide exploratory data visualization tools for researchers, administrations, practitioners and general public to understand the data and to reveal knowledge intuitively.

NeighborVis is a visual analytics system of geospatial-semantic event data in localized neighborhoods within a larger city, town, suburb or rural area. The geospatial-semantic event data (we name it GSE data), consists of geospatial (longitude/latitude or locational address), time, and semantic dimensions, which forms a large part of the important information people deal with for business, personal and administrative use.

The GeoVisuals software system and a Web-based GeoVisuals Data Repository bridge the gap between the diversity of researcher needs and data infrastructure challenges often encountered "in the field". The computing infrastructures help domain researchers and decision-makers capture, manage, query and visualize such big, dynamic data to conduct exploratory and analytical tasks. The utilization of the tools spans across disciplines as diverse as anthropology, clinical health, criminology, disaster management, epidemiology, geography, planning, and sociology. GeoVisuals is developed with collaboration from many domain researchers in academia, various branches of government, and non-profit sectors. It is shared to users as public-licensed software with free access.