Remote sensing products include the generation of a class of geo-maps known as region-based geo-maps, such as choropleth and heatmaps. Comparing those maps is necessary for various real-time application scenarios and ...
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ISBN:
(纸本)9798350354744;9798350354737
Remote sensing products include the generation of a class of geo-maps known as region-based geo-maps, such as choropleth and heatmaps. Comparing those maps is necessary for various real-time application scenarios and geo-maps time series analysis. A major challenge in this process is the vectorization of the raster images, transforming them into a compact data distribution format, in a way that reflects the color themes and densities of the source raster images which represent the geo-maps captured. Aggregation and grouping in such a process is indispensable, which is computationally expensive. To tackle this problem, in this paper, we showcase the design and prototyping of a novel efficient system GeoMapComp, for comparing a specific kind of remote sensing products efficiently, region-based aggregation geo-maps. We specifically compare geo-maps using proxies that are based on geohash encoding, where we apply geohash encoding to divide the geo-map area into equally-sized rectangles, then apply data distribution comparison metrics to compare those proxies, delineating then the differences between maps in a mathematically principled manner, incorporates the geographical characteristics of geo-maps, and is general-purpose and applicable to several kinds of region-based aggregate geo-maps. The paper further contributes by comparing several distance and point-based metrics such as Jenssen-Shannon, KL Divergence, and RMSE. Our results demonstrate the skills of our system in comparing region-based aggregate geo-maps remote sensing products effectively.
smart city applications scenarios, such as traffic monitoring, require regularly generating region-based geographical maps (geo-maps) such as choropleth, to uncover statistical patterns in the data, therefore helping ...
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ISBN:
(纸本)9798350303490
smart city applications scenarios, such as traffic monitoring, require regularly generating region-based geographical maps (geo-maps) such as choropleth, to uncover statistical patterns in the data, therefore helping municipalities to achieve better urban planning. However, with tremendous avalanches of big data arriving in fast streams, it is becoming cumbersome and inefficient to achieve the visualization task in a timely manner. Having said that, spatial approximate query processing presents itself as an indispensable and reliable solution in cases of data overloading. In this paper, we focus on presenting a novel system for generating efficiently region-based geo-maps from fast arriving big georeferenced data streams. We specifically present ApproxGeoViz. It is a system for generating approximate region-based maps from fast arriving data relying on a novel stratified-like spatialsampling method. We have built a standard-compliant prototype and tested its performance on real smart city data. Our results show that ApproxGeoViz is efficient in terms of time-based and accuracy-based QoS constraints such as running time and approximate map accuracy.
Thanks to the wide adoption of GPS-equipped devices, the volume of collected spatialdata is exploding. To achieve interactive exploration and analysis over big spatialdata, people are willing to trade off accuracy f...
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ISBN:
(纸本)9781450383431
Thanks to the wide adoption of GPS-equipped devices, the volume of collected spatialdata is exploding. To achieve interactive exploration and analysis over big spatialdata, people are willing to trade off accuracy for performance through approximation. As a foundation in many approximate algorithms, datasampling now requires more flexibility and better performance. In this paper, we study the spatial independent range sampling (SIRS) problem aiming at retrieving random samples with independence over points residing in a query region. Specifically, we have designed concise index structures with careful data layout based on various space decomposition strategies. Moreover, we propose novel algorithms for both uniform and weighted SIRS queries with low theoretical cost and complexity as well as excellent practical performance. Last but not least, we demonstrate how to support data updates and trade-offs between different sampling methods in practice. According to comprehensive evaluations conducted on real-world datasets, our methods achieve orders of magnitude performance improvement against baselines derived by existing works.
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