Publication: Predictive analysis for cache generation in quadtree based geo visualizations
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2015-10
Defense date
2015-10-16
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Abstract
The Web Mercator projection is commonly used in geo data visualizations. This
projection shows geospatial information based on some restrictions, but allows to
scale the whole world with a precision of centimeters. One of the most important
restrictions is the clustering of the information within cells inside map tiles, so that
they can be cached. Depending on the data that is being visualized, that cache must
behave in a different way to be responsive to user requests.
Web mapping has become a very relevant market in the last few years, and it is
expected to grow even more in the near future, with a great variety of applications
such as self-driving cars or business analytics. The analysis of user reactions and
interactions against a map filled with information will be key when building the
mapping platforms of the future.
This project aims to predict usage patterns in web map visualizations so that
dynamically generated data can be precached to improve the usability of such
maps. To that end, we use different statistical approaches, all of them trained with
real data of map usage activity.
Throughout this document, we describe a suitable way to curate the raw data
acquired through thousands of log files and how to obtain statistical insights from
it. Based on this data, we define a predictive model for tile caching, and we test it
against real map usage data. We also cover the timing details of tile generation,
comparing the efficiency of serving cached tiles against generating tile images on
runtime. The economic impact of the developed solution is also explained in terms
of how much it costs to store the tiles predicted by our caching algorithm in a
content delivery network. Regarding the social context, we analyse which legal
frameworks regulate the web mapping industry and how do them affect our
caching solution.
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Keywords
Web Mercator projection, Geo data visualizations, Predictive model, Geospatial information