Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here

Towards Querying and Visualization of Large Spatio-Temporal Databases

In any database model, data analysis can be eased by extracting a smaller set of the data of interest, called subset, from the mammoth original dataset. Thus, a subset helps enhance the performance of a system by avoiding the iteration through the huge parental data in further analysis. A subset, its specification, or the formal process for its extraction can be complex. In the database community, subsets are extracted through SQL-like queries and through visualization in the Geographic Information System (GIS) community. Both are iterative processes. An SQL query can be a composition of subqueries. Each subquery can be seen as an iterative step toward the extraction of the desired subset. For this to work, subqueries should result into relations that have the same structure as the relations in a given data model. Although it may not be immediately obvious, the visualization can be iterative too. Each community works in its own compartment. Either one uses subprocesses that are only subqueries or only visual interactions. Mixing these two subprocesses would yield a more powerful expressibility in the hands of users. Parametric Data Model is well-known for handling multidimensional parametric data, such as spatial, temporal, or spatio-temporal. In the parametric approach, the object is modeled as a single tuple, creating one-to-one correspondence between an object in the real world and a tuple in the database. The parametric approach relies on its own SQL-like, but richer, query language called ParaSQL which mimics the classical SQL. However, it is simpler and avoids self-join operations; hence, enhances performance. In the parametric approach, the attribute values are defined as a function, allowing large values, also. The execution of a query in the existing prototype of the Parametric Data Model results in data out, as stream in a raw text format that cannot be queried further. This is unlike classical databases, where a subset provides additional strength to a system and the prototype lacks this potential functionality. The real power of ParaSQL lies in the where clause, and previous versions of the prototype had a very simple implementation. It is expanded further in this research work to harness its hidden potential. To perform the preliminary investigation, exploratory visual analysis is an important aspect in any spatio-temporal database system. Previous versions of the prototype of Parametric Data Model completely lacked the visualization functionality. This work ensures the output of a ParaSQL (possibly a subset) will be a relation having the same format as relations in the model rather than plain text. It also attempts to expand the power of the where clause, ensuring a clean logic and more generic nature. Some important basic steps are taken to bring a visual in a way that is conducive to the structures in Parametric Data Model. The richness of GIS visualization serves as the foundation for the visual functionality of the Parametric Data Model. The query is executed on the parametric side, while the results are visualized on GIS side. This integration equips the Parametric Data Model with visualization functionality. GIS visualization also offers a click-based selection of a subset and its persistence, which later can be consumed by Parametric Data Model also. This research work establishes a two-way communication between the two communities-Parametric Data Model and GIS- where the output of one can serve as the input for the other and is an attempt to bring them together

Spatial approaches to information search

Searching for information is a ubiquitous activity, performed in a variety of contexts and supported by rapidly evolving technologies. As a process, information search often has a spatial aspect: spatial metaphors help users refer to abstract contents, and geo-referenced information grounds entities in physical space. While information search is a major research topic in computer science, GIScience and cognitive psychology, this intrinsic spatiality has not received enough attention. This article reviews research opportunities at the crossroad of three research strands, which are (1) computational, (2) geospatial, and (3) cognitive. The articles in this special issue focus on interface design for spatio-temporal information, on the search for qualitative spatial configurations, and on a big-data analysis of the spatial relation “near”

User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks

International audienceIn this paper we propose a procedure consisting of a first collection phase of social net- work messages, a subsequent user query selection, and finally a clustering phase, de- fined by extending the density-based DBSCAN algorithm, for performing a geographic and temporal exploration of a collection of items, in order to reveal and map their latent spatio-temporal structure. Specifically, both several geo-temporal distance measures and a density-based geo-temporal clustering algorithm are proposed. The approach can be applied to social messages containing an explicit geographic and temporal location. The algorithm usage is exemplified to identify geographic regions where many geotagged Twitter messages about an event of interest have been created, possibly in the same time period in the case of non-periodic events (aperiodic events), or at regular timestamps in the case of periodic events. This allows discovering the spatio-temporal periodic and aperiodic characteristics of events occurring in specific geographic areas, and thus increasing the awareness of decision makers who are in charge of territorial planning. Several case studies are used to illustrate the proposed procedure

Proceedings of the Academic Track at State of the Map 2019 - Heidelberg (Germany), September 21-23, 2019

State of the Map featured a full day of academic talks. Building upon the motto of SotM 2019 in "Bridging the Map" the Academic Track session was aimed to provide the bridge to join together the experience, understanding, ideas, concepts and skills from different groups of researchers, academics and scientists from around the world. In particular, the Academic Track session was meant to build this bridge that connects members of the OpenStreetMap community and the academic community by providing an open passage for exchange of ideas, communication and opportunities for increased collaboration. These proceedings include 14 abstracts accepted as oral presentations and 6 abstracts presented as posters. Contributions were received from different academic fields, for example geography, remote sensing, computer and information sciences, geomatics, GIScience, the humanities and social sciences, and even from industry actors. We are particularly delighted to have included abstracts from both experienced researchers and students. Overall, it is our hope that these proceedings accurately showcase the ongoing innovation and maturity of scientific investigations and research into OpenStreetMap, showing how it as a research object converges multiple research areas together. Our aim is to show how the sum total of investigations of issues like Volunteered Geographic Information, geo-information, and geo-digital processes and representation shed light on the relations between crowds, real-world applications, technological developments, and scientific research