Geospatial Big Data Analytics

Permanent URI for this collection


Recent Submissions

Now showing 1 - 6 of 6
  • Item
    Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models
    ( 2023-01-03) García Pereira, Agustín ; Porwol, Lukasz ; Ojo, Adegboyega
    High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modest-size, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative ground-truth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work.
  • Item
    Spatial Analytics with Hospitality Big Data: Examining the Impact of Locational Determinants on Customer Satisfaction in the U.S. Hotel Market
    ( 2023-01-03) Lee, Minwoo ; Kim, Jinwon ; Shin, Hyejo
    Although hotel location has been recognized as one of the important factors affecting hotel selection and guest satisfaction, relatively few studies have examined guest satisfaction with hotel location and its locational determinants at a macro level. This study aims to identify the locational determinants of hotel guest satisfaction through big data spatial analytics via a case study of 5,302 hotels in 151 cities in the U.S. Based on the framework of hotel location satisfaction, we classified all location-related factors into three categories: accessibility to points of interest, transport convenience, and surrounding environment. Our findings indicated that hotel property’s proximity to city area, landmark, park, shopping center, and highway as well as, attraction-driven tourism industry specialization, and hotel industry agglomeration were significant determinants. Furthermore, the impacts of these factors were spatially heterogeneous. These findings can provide geographical insights that are critical for developing a customer service experience and satisfaction model.
  • Item
  • Item
    Big Data Guided Resources Businesses – Leveraging Location Analytics and Managing Geospatial-temporal Knowledge
    ( 2023-01-03) Nimmagadda, Shastri ; Ochan, Andrew ; Reiners, Torsten ; Mani, Neel
    Location data rapidly grow with fast-changing logistics and business rules. Due to fast-growing business ventures and their diverse operations locally and globally, location-based information systems are in demand in resource industries. Data sources in these industries are spatial-temporal, with petabytes in size. Managing volumes and various data in periodic and geographic dimensions using the existing modelling methods is challenging. The current relational database models have implementation challenges, including the interpretation of data views. Multidimensional models are articulated to integrate resource databases with spatial-temporal attribute dimensions. Location and periodic attribute dimensions are incorporated into various schemas to minimise ambiguity during database operations, ensuring resource data's uniqueness and monotonic characteristics. We develop an integrated framework compatible with the multidimensional repository and implement its metadata in resource industries. The resources’ metadata with spatial-temporal attributes enables business research analysts a scope for data views’ interpretation in new geospatial knowledge domains for financial decision support.
  • Item
    The Evolution of Corporate Location Planning: A Survey Approach
    ( 2023-01-03) Aversa , Joe ; Hernandez, Tony
    The unprecedented growth of big data has provided opportunities for the enhancement of retail location decision-making (RLDM) activities. Through a survey of Canadian retail location decision makers, this study examines the current state and progress in: (1) the type and scale of location decisions that retail firms undertake; and (2) the availability and use of geospatial big data and analytics within the decision-making process. The study finds significant increases in the usage of geospatial big data and analytics within corporate location planning. RLDM approaches have expanded to include new data sources, such as social media and mobile location data. With technology redefining consumption behaviors, the retail sector is looking to better understand how best to serve consumers in a market experiencing significant changes to the ways consumers shop. With granular level data being integrated into RLDM a skills gap is emerging in terms of handling and analyzing geospatial big data.
  • Item
    A User-POI-Guide Cost Optimization Method for Tourism Planning Considering Social Distance and User Preferences
    ( 2023-01-03) Li, Da ; Panote, Siriaraya ; Wang, Yuanyuan ; Kawai , Yukiko
    MaaS (Mobility as a Service) itself has come into common use, and these developments have attracted keen interest from the industry in recent years. MaaS can be applied as a solution to deal with the current situation by considering the social distance. However, due to the time-share mechanism, personal assets are monopolized by specific users for a long time that cannot be shared with other users at the same time. Thus, the sharing economy companies in the tourism industry (e.g., Airbnb Experience and Huber) are in a dilemma of low productivity and high cost. In this research, we propose a new travel guide sharing service that considers the concept of social distance and user preferences. The user side only needs to select simple conditions such as travel time and the number of POIs (Point of Interest) that she/he plans to visit, meanwhile, the guide side simply inputs the POIs that she/he can guide. Furthermore, by analyzing these basic information, our proposed system can recommend the tour guides, scenic spots, and route planning to provide a real-time tour guide plan, which addressed the user's preferences and reduced the face-to-face communication to users in advance. To verify the effectiveness of our proposed method, we also ask 68 users to evaluate our system and analyze the results of questionnaires.