Mobile Collaborative Heatmapping to Infer Self-Guided Walking Tourists’ Preferences for Geomedia

Sasaki, Iori; Lu, Min; Sato, Ryosuke

doi:10.3390/ijgi12070283

Cited by 2 publications

(1 citation statement)

References 41 publications

(52 reference statements)

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“…[41] applies the DP algorithm for generating trajectory data for thematic heatmaps at the city scale for tourist activity analysis. Similarly, authors in [42] have designed an approach for the efficient generation of heatmaps using methods based on the DP algorithm. Also, ref.…”

Section: Applications Of Line Simplification In Approximate Geospatia...mentioning

confidence: 99%

Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data

Al Jawarneh,

Foschini,

Bellavista

2023

Sensors

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The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.

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Section: Applications Of Line Simplification In Approximate Geospatia...mentioning

confidence: 99%

Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data

Al Jawarneh,

Foschini,

Bellavista

2023

Sensors

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A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

Diri,

Yildirim

2024

Arab J Sci Eng

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Highly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$ - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.

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Mobile Collaborative Heatmapping to Infer Self-Guided Walking Tourists’ Preferences for Geomedia

Cited by 2 publications

References 41 publications

Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data

Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data

A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

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