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1. RINX: A SOLUTION FOR INFORMATION EXTRACTION FROM BIG RASTER DATASETS

   https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-245-2022  

   Kakkar, D.; Blossom, J.; Guan, W. (January 2022, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. Processing Earth observation data modelled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle the processing, and new approaches are needed to analyse these datasets. The objective of this work is to develop methods to interactively analyse big raster datasets with the goal of most efficiently extracting vector data over specific time periods from any set of raster data. In this paper, we describe RINX (Raster INformation eXtraction) which is an end-to-end solution for automatic extraction of information from large raster datasets. RINX heavily utilises open source geospatial techniques for information extraction. It also complements traditional approaches with state-of-the- art high-performance computing techniques. This paper discusses details of achieving big temporal data extraction with RINX, implemented on the use case of air quality and climate data extraction for long term health studies, which includes methods used, code developed, processing time statistics, project conclusions, and next steps. 

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2. Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

   https://doi.org/10.1186/s12942-020-00201-9  

   Wang, Jue; Kwan, Mei-Po (December 2020, International Journal of Health Geographics)

   null (Ed.)

   Abstract Background Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. Methods This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. Results The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations. Conclusions This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing. 

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3. Scenic routing navigation using property valuation

   https://doi.org/10.1186/s40537-023-00736-1  

   Rishe, Naphtali; Amini, M. Hadi; Adjouadi, Malek (May 2023, Journal of Big Data)

   Abstract Extensive prior work has provided methods for the optimization of routing based on weights assigned to travel duration, and/or travel cost, and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph in order to find the best route. Some users desire that the routing suggestion include consideration pertaining to the scenic-architectural quality of the path. For example, a user may seek a leisure walk via what they might deem as visually attractive architecture. Here, we are proposing a method to quantify such user preferences and scenic quality and to augment the standard routing methods by giving weight to the scenic quality. That is, instead of suggesting merely the time and cost-optimal route, we will find the best route that is tailored towards the user’s scenic quality preferences as an additional criterion to the time and cost. The proposed method uniquely weighs the scenic interest or residential street segments based on the property valuation data. 

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4. Decentralized Data Collection via Swarm Contracts: Study on Crowd-Sourced Google Maps

   https://doi.org/10.1109/ICCAR57134.2023.10151727  

   Mallikarachchi, Sanjaya; Ho, Bonnie; Kanj, Iyad; Seneviratne, Oshani; Godage, Isuru S. (April 2023, 2023 9th International Conference on Control, Automation and Robotics (ICCAR))

   “Data is the new oil” has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting this data can be challenging and costly. To address this issue, we leverage a novel distributed crowdsourcing framework - termed Swarm Contracts - that utilizes blockchain and is applied to robotics technologies. The framework encourages an incentivized crowdsourcing model through open-source robots and a secure, decentralized, and transparent blockchain-based incentive system. As a demonstration of the framework's capabilities, we use it to collect Google Street View ® map data, which can be a resource-intensive task to keep up to date using traditional centralized methods. Our Swarm Contract framework uses Google Street View ® Publish API, which allows for the contribution of street view data to Google Maps @to implement the incentive-based crowdsourcing of street view images. By incorporating a swarm contract-powered framework with the Google Street View ® Publish API, we show that the incentivized crowdsourcing of street view data can be a practical solution to maintain accurate and up-to-date Google Street View ® maps. 

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5. Tradeoffs between safety and time: A routing view

   https://doi.org/10.1016/j.trc.2019.09.020  

   Carmody, Daniel R.; Sowers, Richard B. (November 2019, Transportation research Part C Emerging technologies)

   This article proposes a data-driven combination of travel times, distance, and collision counts in urban mobility datasets, with the goal of quantifying how intertwined traffic accidents are in the road network of a city. We devise a bi-attribute routing problem to capture the tradeoff between travel time and accidents. We apply this to a dataset from New York City. By visualizing the results of this computation in a normalized way, we provide a comparative tool for studies of urban traffic. 

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