617 research outputs found
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
Hierarchical Filter and Refinement System Over Large Polygonal Datasets on CPU-GPU
In this paper, we introduce our hierarchical filter and refinement technique that we have developed for parallel geometric intersection operations involving large polygons and polylines. The inputs are two layers of large polygonal datasets and the computations are spatial intersection on a pair of cross-layer polygons. These intersections are the compute-intensive spatial data analytic kernels in spatial join and map overlay computations. We have extended the classical filter and refine algorithms using PolySketch Filter to improve the performance of geospatial computations. In addition to filtering polygons by their Minimum Bounding Rectangle (MBR), our hierarchical approach explores further filtering using tiles (smaller MBRs) to increase the effectiveness of filtering and decrease the computational workload in the refinement phase. We have implemented this filter and refine system on CPU and GPU by using OpenMP and OpenACC. After using R-tree, on average, our filter technique can still discard 69% of polygon pairs which do not have segment intersection points. PolySketch filter reduces on average 99.77% of the workload of finding line segment intersections. PNP based task reduction and Striping algorithms filter out on average 95.84% of the workload of Point-in-Polygon tests. Our CPU-GPU system performs spatial join on two shapefiles, namely USA Water Bodies and USA Block Group Boundaries with 683K polygons in about 10 seconds using NVidia Titan V and Titan Xp GPU
Conflating point of interest (POI) data: A systematic review of matching methods
Point of interest (POI) data provide digital representations of places in the
real world, and have been increasingly used to understand human-place
interactions, support urban management, and build smart cities. Many POI
datasets have been developed, which often have different geographic coverages,
attribute focuses, and data quality. From time to time, researchers may need to
conflate two or more POI datasets in order to build a better representation of
the places in the study areas. While various POI conflation methods have been
developed, there lacks a systematic review, and consequently, it is difficult
for researchers new to POI conflation to quickly grasp and use these existing
methods. This paper fills such a gap. Following the protocol of Preferred
Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a
systematic review by searching through three bibliographic databases using
reproducible syntax to identify related studies. We then focus on a main step
of POI conflation, i.e., POI matching, and systematically summarize and
categorize the identified methods. Current limitations and future opportunities
are discussed afterwards. We hope that this review can provide some guidance
for researchers interested in conflating POI datasets for their research
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