Location Intelligence

Location Analytics is an important set of spatial analytics theories, concepts, models, and technologies that have become widely known in the public, private, and nonprofit sectors. During the covid-19 pandemic, location analytics has been highlighted through critically-important and now renowned mapping platforms and dashboards, such as the Johns Hopkins Covid-19 Dashboard [1] and the US Center for Disease Control’s Covid Data Tracker [2], as well as by governments worldwide and corporations supporting Covid-19 testing, vaccinations, and supply chains.

Following its inaugural year at HICCS-54, the location intelligence track this year has expanded to three minitracks and eleven accepted papers. The three mini-tracks are (i) Location Analytics for Systems Sciences Research, (ii) Geospatial Big Data Analytics, and (iii) GIS, Industry 4.0, and Sustainability. Together, the three minitracks capture the important role that location data and contemporary location analytics play in aiding our place-based understanding of complex problems at the intersection of the environment, business, and society at large.

These eleven papers may be categorized into three areas within location analytics research: (1) location-based systems for analyzing and mitigating emergencies, (2) location analytics studies of the business impacts of the Covid-19 pandemic, and (3) novel theories, frameworks, and systems for location analytics.

Location-based systems to analyze and mitigate emergencies

Drug abuse in the U.S. has become rampant, with the urgent need to answer the questions of why, where, when, and how to mitigate drug abuse and mortality. The track paper by J. Lee et al. studies the opioid emergency in the state of Ohio based on emergency medical service (EMS) data for Cincinnati based on K-means clustering and random-forest regression, discovering clusters and further characterizing them and offering mitigation measures. The paper by A. Murray et al. concerns deadly wildfires, presenting a locational analytical approach to transition landscapes to be more fire resistant. Finally, the contribution by H. Shimizu et al. develops a model of location-based scheduling of evacuation shelters, testing it for the Kobe City earthquake in Japan to find optimal shelter locations.

Location analytics studies of the impacts on business from the Covid-19 pandemic

The Covid-19 pandemic influenced many aspects of location intelligence and analytics, and two papers concern business impacts. The paper by N. Neuteboom et al. uses big data on transactions from ABN AMRO bank to analyze and compare first and second waves of covid-19 on how and where customers consumed physical bank services. The paper by J. Aversa et al. considers the pressures put on retailers from covid-based government restrictions based on mobile device data, identifying differences in visitation patterns over time and space by retail types.

Novel theories, frameworks and systems for location analytics

The field of location analytics is undergoing rapid innovation. In addition to innovations in the aforementioned papers, four papers focus on innovative contributions to theory and practice. The paper by T. Heuwinkel et al. provides an enhanced method to predict real estate appraisals based on text analytics of geo-referenced Wikipedia articles. This novel approach is tested and shown to increase the accuracy of machine learning for home appraisals. The study by T. Stadler et al. provides a new geometric method to find optimal locations for bus stops based on Voronoi diagrams and instantiates it for the town of German city Roding and surrounding area. Among other benefits, the method saves commuting time in the “first mile,” from home to bus stop. The contribution by T.J. Bihl et al. offers new methods and algorithms to achieve reinforcement learning for a general location intelligence routing task through managing sensor resources. The methods are applied to improve search and rescue operations by unmanned aerial vehicles. Finally an investigation by C. Franklin and J. Sreedharan offers an innovative approach in developing robust GIS implementation by recommending how to add risk analysis during the planning and operational phases for GIS projects.

The Location Intelligence Track in its second year informs the HICSS community about the immense potential of this cutting-edge area of location intelligence in providing novel insights and new directions of research and innovation in the system sciences. This can guide practical government policies, spur business R&D, and provide elegant solutions to complex problems at the intersection of industry, government, and society. We welcome your participation and thoughts as we seek to engage HICSS researchers in developing the field further as an essential part of the system sciences.

References

[1] Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infectious Diseases. 20(5), May 1, 2020, 533-534. doi: 10.1016/S1473-3099(20)30120-1
[2] COVID Data Tracker, Centers for Disease Control and Prevention, available at https://covid.cdc.gov/covid-data-tracker/#datatracker-home

Thomas A. Horan
University of Redlands
Thomas.Horan@redlands.edu