Location Intelligence Research

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Now showing 1 - 10 of 10
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    Spatial Location and Air Transport Connections: The Case of Minnesota’s Medical Device Industry Cluster
    ( 2021-01-05) Munnich, Lee ; Fried, Travis ; Cho, Joanne ; Horan, Thomas
    Minnesota’s medical device industry cluster is not only one of the biggest driving economic forces in the state, but also a global leader in the development and distribution of medical devices. The study assessed the medical device industry’s spatial development and air transport implications, both in terms of industry cluster location and flow of products. The spatial analysis showed that most medical device companies are located within the seven-county Twin-Cities region, but that industries linked to the medical device industry cluster are much more dispersed throughout the state. Regarding products created, the supply chain of medical devices is highly dependent on Minneapolis-St Paul Airport (MSP), which plays a key role in their delivery of medical devices. Air Cargo analysis reveals the high value of medical devices. exports to various locations and raises issues about the future role of air cargo through MSP to the economic prosperity of the industry cluster.
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    Replicability Challenges in Location Analytics
    ( 2021-01-05) Murray, Alan
    The advances in GIS capabilities have been significant over the past few decades, with commercial and open-source software providing relatively easy access to location analytics for even the most novice of users. While many computational methods are indeed accessible and widely applied, this paper focuses on spatial optimization based location analytics available through GIS because of their increased adoption to address a range of social and environmental issues. The significance of this is that insights, management, planning, decision making and policy results are informed by location analytics. Important questions arise, however, about appropriateness, assumptions and validity, especially when user-friendly point-and-click software is involved. Replicability is at the heart of concerns when social and environmental issues are addressed using GIS-based location analytics. Much interest has been devoted to data uncertainty, frame dependency, modifiable areal unit problem and the theoretical assumptions of developed methods, but little attention has been given to definition and implementation details for many advanced location analytics that can be found in GIS. This paper explores these issues as commercial and open-source GIS software incorporate a wide range of optimization based location analytics may face challenges in being replicable, reliable or reproducible.
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    Reducing Transport Miles Through the Use of Mobile Hubs: A Case Study in Local Food Supply Chains
    ( 2021-01-05) Sanders, Isabella ; Montreuil, Benoit
    Though environmentally friendly in many regards, local supply chains are often inefficient due to lack of proper infrastructure. This paper explores the use and placement of mobile hubs for consolidation and distribution of goods in local supply chains. Specifically, we look at local food supply chains where food typically travels from rural farms to suburban and urban restaurants. Currently, consolidation is minimal and not optimized in these supply chains. This paper computes suitability and location analysis through a novel multi-criterion scoring methodology utilizing kernel density and network analysis. The effectiveness of these mobile hubs is assessed through strategic routing, where the routes are optimized for time and distance. Results indicate that on average mobile hubs do in fact reduce mileage and number of stops, lessening emissions in addition to saving time and money. The proposed methodology can be implemented in other local supply chains to better consolidate and distribute goods.
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    Privacy in Transport? Exploring Perceptions of Location Privacy Through User Segmentation
    ( 2021-01-05) Becker, Ingolf ; Posner, Rebecca ; Islam, Tasmina ; Ekblom, Paul ; Borrion, Hervé ; Mcguire, Michael ; Li, Shujun
    Unanticipated accumulation and dissemination of accurate location information flows is the latest iteration of the privacy debate. This mixed-methods research contributes a grounded understanding of risk perceptions, enablers and barriers to privacy preserving behaviour in a cyber-physical environment. We conducted the first representative survey on internet privacy concerns, cyber and physical risk taking, privacy victimisation, usage of location sharing apps and transport choices in the UK with 466 participants. The responses segregated participants into four distinct, novel clusters (cyber risk takers, physical risk takers, transport innovators, and risk abstainers) with cross-validated prediction accuracy of 92%. In the second part of the study, we qualitatively explored these clusters through 12 homogeneous focus groups with 6 participants each. The predominant themes of the groups matched their clusters with little overlap between the groups. The differences in risk perception and behaviours varied greatly between the clusters. Future transport systems, apps and websites that rely on location data therefore need a more personalised approach to information provision surrounding location sharing. Failing to recognise these differences could lead to reduced data sharing, riskier sharing behaviour or even total avoidance of new forms of technology in transport.
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    Kartta Labs: Collaborative Time Travel
    ( 2021-01-05) Tavakkol, Sasan ; Han, Feng ; Mayer, Brandon ; Phillips, Mark ; Shahabi, Cyrus ; Chiang, Yao-Yi ; Kiveris, Raimondas
    We introduce the modular and scalable design of Kartta Labs, an open source, open data, and scalable system for virtually reconstructing cities from historical maps and photos. Kartta Labs relies on crowdsourcing and artificial intelligence consisting of two major modules: Maps and 3D models. Each module, in turn, consists of sub-modules that enable the system to reconstruct a city from historical maps and photos. The result is a spatiotemporal reference that can be used to integrate various collected data (curated, sensed, or crowdsourced) for research, education, and entertainment purposes. The system empowers the users to experience collaborative time travel such that they work together to reconstruct the past and experience it on an open source and open data platform.
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    Geographic Data Informs Funding and Management of Metro Bike Share System
    ( 2021-01-05) Schmidt, Pamela ; Feng, Jia ; Freeze, Ronald
    Geographic data is often used to supplement business data, but geographic location (GeoLocation) data has business value in its own right. This geo-spatial study presents a midwestern town’s use of location analytics to infer the purpose for bike trips (usage purpose) and perform what-if analysis to enhance transportation options. The study applies spatial data analysis of bikeshare within transit management and public planning to address funding sources and public good. This case includes GeoExtension of the Metro Bike Share source data by utilizing U. S. Census data. The overall Metro Transit operational goal is to effectively manage the rideshare program to maximize community benefits, particularly in offering optimal transit options. Analysis to inform business operations are 1) inferring likely purpose for bike rides to differentiate between transportation and leisure; 2) determine if bike use integrates with other transit offerings, and 3) to provide transportation options to residents in low-income areas.
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    Exploiting the Temporal Dimension of Remotely Sensed Imagery with Deep Learning Models
    ( 2021-01-05) García Pereira, Agustín ; Porwol, Lukasz ; Ojo, Adegboyega ; Curry, Edward
    The rapid rise of artificial intelligence and the increasing availability of open Earth Observation (EO) data present new opportunities to address important global problems such as the proliferation of agricultural systems which endanger ecological sustainability. Despite the plethora of satellite images describing a given location on earth every year, very few deep learning-based solutions have harnessed the temporal and sequential dynamics of land use to map agricultural practices. This paper compares different approaches to classify agricultural land use exploiting the temporal and spectral dimensions of EO data. The results show greater efficiency of the presented deep learning-based algorithms compared to state-of-the-art approaches when mapping agricultural classes.
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    Enterprise Solutions Criteria in the Age of GeoBlockchain: Land Ownership and Supply Chain
    ( 2021-01-05) Papantoniou, Constantinos ; Hilton, Brian
    Land ownership and supply chain use cases are an enormous business challenge for both the public and private sectors. Every organization has different needs and wants, and industry leaders are researching and exploring ways to improve and impact their business transaction processes. Blockchain and Geospatial technologies are two tools that could help an organization add value in this manner. The combination of blockchain and geospatial technologies would result in the new concept of GeoBlockchain, defined here as a solution artifact that could be used to trace the trends and behaviors of participants (users) geographically and spatially, based on distributed nodes, transactions, and geo-locations via blockchain technology. The result of this research was the design, development, and implementation of two enterprise solution prototypes for land ownership and supply chains. This research indicates that blockchain technology can be integrated with geospatial technology, resulting in the GeoBlockchain implementation.
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    Challenges Posed by Locational Data Privacy
    ( 2021-01-05) Ahmad, Rizwan ; Gal, Uri
    With the growth of innovative positioning technologies, research into individuals’ behavioral challenges posed by location-based services has become increasingly popular in recent years. Scholars from various social sciences and management disciplines have attempted to address such challenges in order to understand and mitigate concerns for locational-data privacy. In view of the broad applicability of location-based services, we conduct a review of eight prominent IS journals to investigate and understand individuals’ behavioral challenges in using such services. Our review reveals that perception of individuals’ locational-data privacy is constantly influenced by their respective social norms, social reality, and cultural background as well as their current geographical or locational factor. In light of this finding, we outline possible directions and opportunities for further IS research around three philosophical approaches- “positivist”, “interpretivist”, and “critical”- with the aim of enriching our discussion of how and why individuals’ social reality and cultural factors influence their perception of locational- data privacy.
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    Introduction to the Minitrack on Location Intelligence Research
    ( 2021-01-05) Pick, James ; Sarkar, Avijit