Linear street extraction using a Conditional Random Field model

A novel method for extracting linear streets from a street network is proposed where a linear street is defined as a sequence of connected street segments having a shape similar to a straight line segment. Specifically a given street network is modeled as a Conditional Random Field (CRF) where the task of extracting linear streets corresponds to performing learning and inference with respect to this model. The energy function of the proposed CRF model is submodular and consequently exact inference can be performed in polynomial time. This contrasts with traditional solutions to the problem of extracting linear streets which employ heuristic search procedures and cannot guarantee that the optimal solution will be found. The performance of the proposed method is quantified in terms of identifying those types or classes of streets which generally exhibit the characteristic of being linear. Results achieved on a large evaluation dataset demonstrate that the proposed method greatly outperforms the aforementioned traditional solutions

A Geovisual Analytics Approach for Mouse Movement Analysis

The use of Web maps has created opportunities and challenges for map generation and delivery. While volunteered geographic information has led to the development of accurate and inexpensive Web maps, the sheer volume of data generated has created spatial information overload. This results in difficulties identifying relevant map features. Geopersonalisation, which adapts map content based on user interests offers a solution to this. The technique is especially powerful when implicit indicators of interest are used as a basis for personalisation. This article describes the design and features of VizAnalysisTools, a suite of tools to visualise and interpret users’ implicit interactions with map content. While traditional data mining techniques can be used to identify trends and preferences, visual analytics, in particular Geovisual Analytics, which assists the human cognition process, has proven useful in detecting interesting patterns. By identifying salient trends, areas of interest on the map become apparent. This knowledge can be used to strengthen the algorithms used for Geopersonalisation

Volunteered and Crowdsourced Geographic Information: the OpenStreetMap Project

Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the potential of such an initiative goes well beyond map-making: ranging from projects such as the Humanitarian OpenStreetMap (HOT) project, that understands itself as a bridge between the OSM community and humanitarian responders, to collaborative projects such as Mapillary, where citizens take street-level images and the system aims to automate mapping. A common trend among these projects using OSM is the fact that the community dynamic tends to create spin-off projects. Currently, we see a drive towards projects that support sustainability goals using OSM. We discuss some such applications and highlight challenges posed by this new paradigm. We also explore the most promising future uses of this increasingly popular participatory phenomenon

Volunteered and crowdsourced geographic information: the OpenStreetMap project

Advancements in technology over the last two decades have changed how spatial data are created and used. In particular, in the last decade, volunteered geographic information (VGI), i.e., the crowdsourcing of geographic information, has revolutionized the spatial domain by shifting the map-making process from the hands of experts to those of any willing contributor. Started in 2004, OpenStreetMap (OSM) is the pinnacle of VGI due to the large number of volunteers involved and the volume of spatial data generated. While the original objective of OSM was to create a free map of the world, its uses have shown how the potential of such an initiative goes well beyond map-making: ranging from projects such as the Humanitarian OpenStreetMap (HOT) project, that understands itself as a bridge between the OSM community and humanitarian responders, to collaborative projects such as Mapillary, where citizens take street-level images and the system aims to automate mapping. A common trend among these projects using OSM is the fact that the community dynamic tends to create spin-off projects. Currently, we see a drive towards projects that support sustainability goals using OSM. We discuss some such applications and highlight challenges posed by this new paradigm. We also explore the most promising future uses of this increasingly popular participatory phenomenon

A Web and Mobile System for Environmental Decision Support

Current field data collection methods for many of today’s scientific and other observer/monitor type applications are still entrenched in the “clipboard age”, requiring manual data transcription to a database management system at some (often much) later date, and only allows for visualisation and analysis of recently captured field data “back in the lab”. This chapter is targeted at progressing today’s pen & paper methodology into the spatially enabled mobile computing age of realtime multi-media data input, integration, visualisation, and analysis simultaneously both in the field and the lab. The system described is customized to the specific needs of the Canadian Great Lakes Laboratory for Fisheries and Aquatic Sciences Fish Habitat Management Group requirements for fish species at risk assessment, but is ready for adaptation to other environmental agency applications (e.g. forestry, health-pesticide monitoring, agriculture, etc.). The chapter is ideally suited to all agencies responsible for collecting field data of any type that have not yet moved to a state-of-the-art mobile and wireless data collection, visualisation, and analysis work methodolog

Effective Vector Data Transmission and Visualization Using HTML5

In this paper we evaluate the potential of the next major revision of HTML (Hypertext Markup Language), that is HTML5, to provide an effective platform for the transmission and visualization of vector based geographical data. Relative to the current version of HTML, HTML 4.01, HTML5 offers an improved platform to perform these tasks through greater interoperability with existing technologies and the introduction of many new API’s. Visualization of vector data can be achieved using the new methods of inline-SVG and the Canvas API. An analysis of the pros and cons of each method is presented. HTML5 introduces a novel WebSocket API which defines a full-duplex communication channel between client and server. This provides improved data communication both in terms of bandwidth utilization and network latency relative to existing push technologies. To demonstrate the effectiveness of HTML5 for vector data delivery a novel selective progressive transmission methodology is implemented using the WebSocket and Canvas API’s

A Web-based and Mobile Environmental Management System

This paper describes a Web-based and mobile system specifically developed to monitor fish species at risk. Such a system integrates spatial functionality to allow users not only to visualise maps and metadata of the area of concern but also to perform context-aware queries and updating of spatial datasets. The spatial datasets are provided by the Canadian Department of Fisheries and Oceans (DFO) and the prototype is customised to the specific needs of the Great Lakes Laboratory for Fisheries and Aquatic Sciences (GLLFAS) Fish Habitat Section requirements for fish species at risk assessment. Currently, researchers, habitat biologists and enforcement officers have access to the fisheries database, containing layers of biological information solely from the office. Delivering these data overlaid on base maps of the Great Lakes region to a GPS-enabled hand-held device and linking it to each task currently being investigated allows for mobile DFO biologists and enforcement officers in the field to make informed decisions immediately. In this paper we describe the system and demonstrate how it is used by the DFO in practice

Mining Spatio-Temporal Datasets: Relevance, Challenges and Current Research Directions

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forerast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the interest of many users has shifted from static views of geospatial phenomena, which capture its “spatiality” only, to more advanced means of discovering dynamic relationships among the patterns and events contained in the data as well as understanding the changes occurring in spatial data over time