TY - JOUR
T1 - KDV-Explorer: A Near Real-Time Kernel Density Visualization System for Spatial Analysis
AU - Chan, Tsz Nam
AU - Ip, Pak Lon
AU - Leong Hou, U.
AU - Tong, Weng Hou
AU - Mittal, Shivansh
AU - Li, Ye
AU - Cheng, Reynold
N1 - Funding Information:
∗This work was supported by the National Key Research and Development Plan of China (No.2019YFB2102100), the Science and Technology Development Fund Macau (SKL-IOTSC-2021-2023, 0015/2019/AKP), University of Macau (MYRG2019-00119-FST), the Research Grants Council of Hong Kong (RGC Projects 17229116 and 17205015), University of Hong Kong (Projects 104005858, 104005994), HKU-TCL Joint Research Center for Artificial Intelligence (Project no. 200009430), and Guangdong-Hong Kong-Macau Joint Laboratory Program 2020 (Project No: 2020B1212030009). Pak Lon Ip, Leong Hou U, Weng Hou Tong and Ye Li are also affiliated with the State Key Laboratory of Internet of Things for Smart City. Reynold Cheng is also affiliated with Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities. This work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing [email protected]. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 14, No. 12 ISSN 2150-8097. doi:10.14778/3476311.3476312
Publisher Copyright:
© The authors.
PY - 2021/7
Y1 - 2021/7
N2 - Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.
AB - Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.
UR - http://vldb.org/pvldb/vol14-volume-info/
UR - http://www.scopus.com/inward/record.url?scp=85116798412&partnerID=8YFLogxK
U2 - 10.14778/3476311.3476312
DO - 10.14778/3476311.3476312
M3 - Conference article
AN - SCOPUS:85116798412
SN - 2150-8097
VL - 14
SP - 2655
EP - 2658
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
T2 - 47th International Conference on Very Large Data Bases, VLDB 2021
Y2 - 16 August 2021 through 20 August 2021
ER -
