Location Intelligence Research

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Now showing 1 - 5 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.