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RgoogleMaps and loa: Unleashing R Graphics Power on Map Tiles

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  • Loecher, Markus
  • Ropkins, Karl

Abstract

The RgoogleMaps package provides (1) an R interface to query the Google and the OpenStreetMap servers for static maps in the form of PNGs, and (2) enables the user to overlay plots on those maps within R. The loa package provides dedicated panel functions to integrate RgoogleMaps within the lattice plotting environment. In addition to solving the generic task of plotting on a map background in R, we introduce several specific algorithms to detect and visualize spatio-temporal clusters. This task can often be reduced to detecting over-densities in space relative to a background density. The relative density estimation is framed as a binary classification problem. An integrated hotspot visualizer is presented which allows the efficient identification and visualization of clusters in one environment. Competing clustering methods such as the scan statistic and the density scan offer higher detection power at a much larger computational cost. Such clustering methods can then be extended using the lattice trellis framework to provide further insight into the relationship between clusters and potentially influential parameters. While there are other options for such map ‘mashups’ we believe that the integration of RgoogleMaps and lattice using loa can in certain circumstances be advantageous, e.g., by providing a highly intuitive working environment for multivariate analysis and flexible testbed for the rapid development of novel data visualizations.

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  • Loecher, Markus & Ropkins, Karl, 2015. "RgoogleMaps and loa: Unleashing R Graphics Power on Map Tiles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i04).
  • Handle: RePEc:jss:jstsof:v:063:i04
    DOI: http://hdl.handle.net/10.18637/jss.v063.i04
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    References listed on IDEAS

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    1. Daniel B. Neill, 2012. "Fast subset scan for spatial pattern detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 337-360, March.
    2. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    3. Almquist, Zack W., 2010. "US Census Spatial and Demographic Data in R: The UScensus2000 Suite of Packages," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i06).
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    2. Handley, John C. & Fu, Lina & Tupper, Laura L., 2019. "A case study in spatial-temporal accessibility for a transit system," Journal of Transport Geography, Elsevier, vol. 75(C), pages 25-36.
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    4. Maximilian Stallkamp & Brian C Pinkham & Andreas P J Schotter & Olha Buchel, 2018. "Core or periphery? The effects of country-of-origin agglomerations on the within-country expansion of MNEs," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 49(8), pages 942-966, October.
    5. Arwa S. Sayegh & Richard D. Connors & James E. Tate, 2018. "Uncertainty Propagation from the Cell Transmission Traffic Flow Model to Emission Predictions: A Data-Driven Approach," Service Science, INFORMS, vol. 52(6), pages 1327-1346, December.
    6. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
    7. Zamar, David S. & Gopaluni, Bhushan & Sokhansanj, Shahab, 2017. "Optimization of sawmill residues collection for bioenergy production," Applied Energy, Elsevier, vol. 202(C), pages 487-495.

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