A Survey of Urban Geographic Information Inference Driven by Crowd-Sourced Spatio-Temporal Data

RUAN Si-jie; XIONG Ke-qin; WANG Shu-liang; GENG Jing; BAO Jie; ZHENG Yu

doi:10.12263/DZXB.20230131

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A Survey of Urban Geographic Information Inference Driven by Crowd-Sourced Spatio-Temporal Data

SURVEYS AND REVIEWS | 更新时间：2025-12-08

- A Survey of Urban Geographic Information Inference Driven by Crowd-Sourced Spatio-Temporal Data
- ACTA ELECTRONICA SINICA Vol. 51, Issue 8, Pages: 2238-2259(2023)
- 作者机构：
  
  1.北京理工大学计算机学院,北京 100081
  2.京东城市(北京)数字科技有限公司,北京 100176
  3.京东智能城市研究院,北京 100176
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62076027;42201461);Beijing Institute of Technology Research Fund Program for Young Scholar(6120220113)
- DOI：10.12263/DZXB.20230131
  CLC： TP391;
- Received：15 February 2023，
  
  Revised：2023-07-17，
  
  Published：25 August 2023
- 稿件说明：
移动端阅览
阮思捷,熊可钦,王树良等.众包时空数据驱动的城市地理信息推测综述[J].电子学报,2023,51(08):2238-2259.

RUAN Si-jie,XIONG Ke-qin,WANG Shu-liang,et al.A Survey of Urban Geographic Information Inference Driven by Crowd-Sourced Spatio-Temporal Data[J].ACTA ELECTRONICA SINICA,2023,51(08):2238-2259.
阮思捷,熊可钦,王树良等.众包时空数据驱动的城市地理信息推测综述[J].电子学报,2023,51(08):2238-2259. DOI： 10.12263/DZXB.20230131.

RUAN Si-jie,XIONG Ke-qin,WANG Shu-liang,et al.A Survey of Urban Geographic Information Inference Driven by Crowd-Sourced Spatio-Temporal Data[J].ACTA ELECTRONICA SINICA,2023,51(08):2238-2259. DOI： 10.12263/DZXB.20230131.

摘要

对地理信息的准确掌握是城市中各种智能决策得以实现的基础.传统地理信息收集主要靠人工测绘、人工巡检或固定传感器感知，设备、人力成本高昂.近年来，随着移动互联网的发展，泛在的移动群体在城市中产生了海量的时空数据，他们有意或无意间成为城市的传感器，使研究人员有机会利用众包的思路基于此类数据推测城市地理信息.基于众包时空数据推测城市地理信息具有成本低、空间覆盖广、更新及时等优点.但其具有严重的数据质量问题，对城市地理信息推测带来了巨大挑战.本文综述了根据轨迹、基于位置的社交网络、街景等众包时空数据，推测城市中以路段、兴趣点、兴趣面为代表的地理实体的位置和属性的方法.本文给出了众包时空数据和地理实体的定义，详细比较了众包时空数据驱动的推测方法与传统方法的优劣，说明了研究问题和挑战，然后讨论了地图匹配、名称提取、位置发现和统计属性推测四个研究问题的研究进展，最后展望了该领域未来的研究方向.

Abstract

Knowing the accurate geographic information is the basis to achieve the intelligent decisions in cities. Traditional geographic information collecting mainly relies on manual mapping

patrolling or sensing by static geographical sensors

which are expensive given specialized equipment and labors. Recently

with the development of the mobile Internet

the ubiquitous moving objects has generated massive spatio-temporal data in the urban spaces

who act as sensors of the city consciously or unconsciously

and make it possible to infer the geographic information based on those data in a crowd-sourced manner. The geographic information inference based on the crowd-sourced spatio-temporal data enjoys the advantages of low cost

high spatial coverage

and timely updates. However

it also has the data quality issues

which introduce great challenges to the urban geographic information inference. In this paper

we survey the location and attribute inference of geospatial entities

including the road network

point of interest and area of interest

based on crowd-sourced spatio-temporal data

e.g.

trajectories

location-based social network

and street views. We give the definitions of crowd-sourced spatio-temporal data and geospatial entities

compare the pros and cons of utilizing the crowd-sourced spatio-temporal data to infer the geographic information against traditional methods

and elaborate the research problems and challenges. After that

we review four research problems

i.e.

map matching

name extraction

location discovery and statistical attribute inference. Finally

we present the future research directions and conclude the paper.

关键词

Keywords

references

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