Computing with Spatial Trajectories

By Yu Zheng, Xiaofang Zhou. Computing with Spatial Trajectories, 2011

612 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/95f8afdb269877cd0a29907430e6b3091b1a81f6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geography, Planning and Development

1. Yu Zheng
2. Xing Xie
3. Mohamed F. Mokbel
4. Jie Bao
5. Ouri Wolfson
6. Licia Capra
7. Hai Yang
8. Jing Yuan

In Transportation

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. David Wilkie
5. Jie Bao
6. Jing Yuan
7. Mohamed F. Mokbel
8. Licia Capra

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. David Wilkie
4. Ouri Wolfson
5. Jie Bao
6. Mohamed F. Mokbel
7. Jing Yuan
8. Licia Capra

Citations in the corpus

• Spatially Weighted Interaction Models (SWIM) (2016, background): “…distance (SOP) suggested by Agrawal, Faloutsos, and Swami (1993) can be computed by simply summing up the distances between the corresponding points: SOP p1 pn; q1 qnð ÞD Xn iD 1d pi; qið Þ; ð28Þ where d pi; qið Þ is the Euclidean distance between points pi and qi (see Zheng and Zhou 2011).”
• Visual Traffic Jam Analysis Based on Trajectory Data (2013, background): “See Zheng et al.'s book [54] for an overview.”
• Constructing popular routes from uncertain trajectories (2012, background): “Without loss of generality, a trajectory is a sequence of data points where each data point records location information and a time-stamp [18].”
• Predicting future locations with hidden Markov models (2012, background): “Many previous works have addressed the issue of computing with spatial trajectories, and a detailed survey is given in the book by Zheng and Zhou [26].”
• Trajectory Data Mining (2015, background): “A small portion of the content of this article is derived from a few chapters of a book, entitled Computing with Spatial Trajectories [157], which was co-edited by Xiaofang Zhou and me.”
• Recommendations in location-based social networks: a survey (2015, background): “, check-ins and geo-tagged photos) reflect more accurately a user’s experiences, living patterns, preferences and interests than the user’s online behaviors [137].”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “nd trajectory classification [23] which have been updated to cover the most recent work in these important areas. The closest related review work is the textbook “Computing with spatial trajectories” [209], published in 2011. Current techniques have advanced significantly in the last ten years, incl”
• Avoiding the crowds: understanding Tube station congestion patterns from trip data (2012, background): “, [12], [5], [20]) since the actual route that a user undertakes between any origin and destination is unknown to us; in many cases, there are a wide variety of candidate routes.”
• Discovering regions of different functions in a city using human mobility and POIs (2012, background): “Human mobility is represented by people’s movement trajectories, which can be cell-tower traces in a cellular network, or trajectories of driving routes, or a sequence of posts (like geo-tweets, geo-tagged photos, or check-ins) in location-based services [22].”
• Urban Computing: Concepts, Methodologies, and Applications (2014, background): “Refer to Chapter 1 of the book [Zheng and Zhou 2011f] for details.”