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Abstract. Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Estimation) to facilitate further usage and research by the wider academic community. 

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.