PROGRESS IN GEOGRAPHY ›› 2022, Vol. 41 ›› Issue (1): 64-72.doi: 10.18306/dlkxjz.2022.01.006
• Special Column: Theories and Methods of Behavioral Geography • Previous Articles Next Articles
Received:
2021-09-03
Revised:
2021-11-27
Online:
2022-01-28
Published:
2022-03-28
Supported by:
YIN Zhangcai, KANG Ziqiang. Advances in kernel density estimation supported by time geography[J].PROGRESS IN GEOGRAPHY, 2022, 41(1): 64-72.
[1] |
Dodge S, Gao S, Tomko M, et al. Progress in computational movement analysis-towards movement data science[J]. International Journal of Geographical Information Science, 2020, 34(12):2395-2400.
doi: 10.1080/13658816.2020.1784425 |
[2] |
Kraemer M U G, Yang C H, Gutierrez B, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China[J]. Science, 2020, 368:493-497.
doi: 10.1126/science.abb4218 pmid: 32213647 |
[3] |
Zhao P X, Jonietz D, Raubal M. Applying frequent-pattern mining and time geography to impute gaps in smartphone-based human-movement data[J]. International Journal of Geographical Information Science, 2021, 35(11):2187-2215.
doi: 10.1080/13658816.2020.1862126 |
[4] | 王亚飞, 袁辉, 陈碧宇, 等. 行程时间不确定环境下地点可达性研究[J]. 武汉大学学报(信息科学版), 2019, 44(11):1723-1729. |
[ Wang Yafei, Yuan Hui, Chen Biyu, et al. Measuring place-based accessibility under travel time uncertainty. Geomatics and Information Science of Wuhan University, 2019, 44(11):1723-1729. ] | |
[5] | 关美宝, 申悦, 赵莹, 等. 时间地理学研究中的GIS方法: 人类行为模式的地理计算与地理可视化[J]. 国际城市规划, 2010, 25(6):18-26. |
[ Kwan Mei-Po, Shen Yue, Zhao Ying, et al. GIS methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Urban Planning International, 2010, 25(6):18-26. ] | |
[6] | 杨倍倍, 曲英杰, 王金鑫. 基于移动数据和时间地理学的大学生行为模式构建与分析[J]. 地理信息世界, 2018, 25(1):77-81. |
[ Yang Beibei, Qu Yingjie, Wang Jinxin. Construction and analysis of college students' behavior patterns based on mobile data and time geography. Geomatics World, 2018, 25(1):77-81. ] | |
[7] | 赵志远, 尹凌, 方志祥, 等. 轨迹数据的时间采样间隔对停留识别和出行网络构建的影响[J]. 武汉大学学报(信息科学版), 2018, 43(8):1152-1158. |
[ Zhao Zhiyuan, Yin Ling, Fang Zhixiang, et al. Impacts of temporal sampling intervals on stay detection and movement network construction in trajectory data. Geomatics and Information Science of Wuhan University, 2018, 43(8):1152-1158. ] | |
[8] |
Yang X P, Fang Z X, Xu Y, et al. Spatial heterogeneity in spatial interaction of human movements: Insights from large-scale mobile positioning data[J]. Journal of Transport Geography, 2019, 78:29-40.
doi: 10.1016/j.jtrangeo.2019.05.010 |
[9] |
Zhao Z Y, Shaw S L, Yin L, et al. The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data[J]. International Journal of Geographical Information Science, 2019, 33(7):1471-1495.
doi: 10.1080/13658816.2019.1584805 |
[10] |
柴彦威, 李春江, 张艳. 社区生活圈的新时间地理学研究框架[J]. 地理科学进展, 2020, 39(12):1961-1971.
doi: 10.18306/dlkxjz.2020.12.001 |
[ Chai Yanwei, Li Chunjiang, Zhang Yan. A new time-geography research framework of community life circle. Progress in Geography, 2020, 39(12):1961-1971. ] | |
[11] |
Shi W Z, Chen P F, Shen X Q, et al. An adaptive approach for modelling the movement uncertainty in trajectory data based on the concept of error ellipses[J]. International Journal of Geographical Information Science, 2021, 35(6):1131-1154.
doi: 10.1080/13658816.2020.1828591 |
[12] | 涂伟, 曹劲舟, 高琦丽, 等. 融合多源时空大数据感知城市动态[J]. 武汉大学学报(信息科学版), 2020, 45(12):1875-1883. |
[ Tu Wei, Cao Jinzhou, Gao Qili, et al. Sensing urban dynamics by fusing multi-sourced spatiotemporal big data. Geomatics and Information Science of Wuhan University, 2020, 45(12):1875-1883. ] | |
[13] |
Bonnier A, Finné M, Weiberg E. Examining land-use through GIS-based kernel density estimation: A re-evaluation of legacy data from the berbati-limnes survey[J]. Journal of Field Archaeology, 2019, 44(2):70-83.
doi: 10.1080/00934690.2019.1570481 |
[14] |
Loraamm R W, Downs J A, Lamb D. A time-geographic approach to quantifying wildlife-road interactions[J]. Transactions in GIS, 2019, 23(1):70-86.
doi: 10.1111/tgis.v23.1 |
[15] |
戚铭尧, 吴涛, 张新. 车辆路径问题: 从时间地理学的视角[J]. 地球信息科学学报, 2015, 17(1):22-30.
doi: 10.3724/SP.J.1047.2015.00022 |
[ Qi Mingyao, Wu Tao, Zhang Xin. Vehicle routing problem: From a perspective of time geography. Journal of Geo-information Science, 2015, 17(1):22-30. ] | |
[16] |
Cheng S W, Xie B, Bie Y M, et al. Measure dynamic individual spatial-temporal accessibility by public transit: Integrating time-table and passenger departure time[J]. Journal of Transport Geography, 2018, 66:235-247.
doi: 10.1016/j.jtrangeo.2017.12.005 |
[17] |
周文娟, 张明锋, 林广发. 失散人员时空信息模糊匹配模型[J]. 地球信息科学学报, 2017, 19(7):886-894.
doi: 10.3724/SP.J.1047.2017.00886 |
[ Zhou Wenjuan, Zhang Mingfeng, Lin Guangfa. A fuzzy matching model of spatial-temporal information of dispersed person. Journal of Geo-information Science, 2017, 19(7):886-894. ] | |
[18] | 张学辉. 基于云平台的寻找失踪老人系统设计与开发[D]. 赣州: 江西理工大学, 2018. |
[ Zhang Xuehui. Design and development of the system for finding missing elders based on cloud platform. Ganzhou, China: Jiangxi University of Science and Technology, 2018. ] | |
[19] |
Downs J, Horner M, Lamb D, et al. Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement[J]. International Journal of Geographical Information Science, 2018, 32(7):1505-1522.
doi: 10.1080/13658816.2017.1421764 |
[20] | Buchin K, Sijben S, Arseneau T J M, et al. Detecting movement patterns using Brownian bridges[C]// Association for Computing Machinery. SIGSPATIAL '12: Proceedings of the 20th international conference on advances in geographic information systems. California, USA, 2012: 119-128. |
[21] |
Song Y, Miller H J. Simulating visit probability distributions within planar space-time prisms[J]. International Journal of Geographical Information Science, 2014, 28(1):104-125.
doi: 10.1080/13658816.2013.830308 |
[22] |
Long J A, Weibel R, Dodge S, et al. Moving ahead with computational movement analysis[J]. International Journal of Geographical Information Science, 2018, 32(7):1275-1281.
doi: 10.1080/13658816.2018.1442974 |
[23] |
Yin Z C, Li S J, Ying S, et al. Method for calculating the encounter probability in network space[J]. Transactions in GIS, 2020, 24(2):402-422.
doi: 10.1111/tgis.v24.2 |
[24] |
Long J A, Nelson T A. A review of quantitative methods for movement data[J]. International Journal of Geographical Information Science, 2013, 27(2):292-318.
doi: 10.1080/13658816.2012.682578 |
[25] | Downs J A. Time-geographic density estimation for moving point objects[M]// Fabrikant S I, Reichenbacher T, van Kreveld M, et al. Geographic Information Science. Berlin, Germany: Springer, 2010: 16-26. |
[26] |
Horne J S, Garton E O, Krone S M, et al. Analyzing animal movements using Brownian bridges[J]. Ecology, 2007, 88(9):2354-2363.
pmid: 17918412 |
[27] |
Hägerstraand T. What about people in regional science?[J]. Papers in Regional Science, 1970, 24(1):7-24.
doi: 10.1111/j.1435-5597.1970.tb01464.x |
[28] | Miller H J, Dodge S, Miller J, et al. Towards an integrated science of movement: Converging research on animal movement ecology and human mobility science[J]. International Journal of Geographical Information Science, 2019, 33(5):855-876. |
[29] |
Long J, Nelson T. Home range and habitat analysis using dynamic time geography[J]. The Journal of Wildlife Management, 2015, 79(3):481-490.
doi: 10.1002/jwmg.845 |
[30] |
Demšar U, Long J A. Potential path volume (PPV): A geometric estimator for space use in 3D[J]. Movement Ecology, 2019, 7:14. doi: 10.1186/s40462-019-0158-4.
doi: 10.1186/s40462-019-0158-4 pmid: 31164985 |
[31] |
Elias D, Kuijpers B. A note on measuring the volume of space-time prisms and the area of their spatial projections[J]. Transactions in GIS, 2020, 24(5):1427-1436.
doi: 10.1111/tgis.v24.5 |
[32] |
Kuijpers B, Technitis G. Space-time prisms on a sphere with applications to long-distance movement[J]. International Journal of Geographical Information Science, 2020, 34(10):1980-2003.
doi: 10.1080/13658816.2020.1738439 |
[33] |
Winter S, Yin Z C. Directed movements in probabilistic time geography[J]. International Journal of Geographical Information Science, 2010, 24(9):1349-1365.
doi: 10.1080/13658811003619150 |
[34] |
Chen B Y, Yuan H, Li Q Q, et al. Spatiotemporal data model for network time geographic analysis in the era of big data[J]. International Journal of Geographical Information Science, 2016, 30(6):1041-1071.
doi: 10.1080/13658816.2015.1104317 |
[35] |
Song Y, Miller H J, Zhou X S, et al. Modeling visit probabilities within network-time prisms using Markov techniques[J]. Geographical Analysis, 2016, 48(1):18-42.
doi: 10.1111/gean.2016.48.issue-1 |
[36] |
Hong I, Murray A T. Efficient measurement of continuous space shortest distance around barriers[J]. International Journal of Geographical Information Science, 2013, 27(12):2302-2318.
doi: 10.1080/13658816.2013.788182 |
[37] |
Yin Z C, Wu Y, Winter S, et al. Random encounters in probabilistic time geography[J]. International Journal of Geographical Information Science, 2018, 32(5):1026-1042.
doi: 10.1080/13658816.2018.1428748 |
[38] |
Kuijpers B, Othman W. Modeling uncertainty of moving objects on road networks via space-time prisms[J]. International Journal of Geographical Information Science, 2009, 23(9):1095-1117.
doi: 10.1080/13658810802097485 |
[39] |
刘钊, 罗智德, 张耀方, 等. 基于时空棱柱的人员搜寻范围优化[J]. 地球信息科学学报, 2014, 16(4):531-536.
doi: 10.3724/SP.J.1047.2014.00531 |
[ Liu Zhao, Luo Zhide, Zhang Yaofang, et al. The optimization of the search area in a search and rescue process based on GIS. Journal of Geo-information Science, 2014, 16(4):531-536. ] | |
[40] |
Nakaya T, Yano K. Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics[J]. Transactions in GIS, 2010, 14(3):223-239.
doi: 10.1111/j.1467-9671.2010.01194.x |
[41] |
Yuan K, Cheng X Q, Gui Z P, et al. A quad-tree-based fast and adaptive kernel density estimation algorithm for heat-map generation[J]. International Journal of Geographical Information Science, 2019, 33(12):2455-2476.
doi: 10.1080/13658816.2018.1555831 |
[42] |
Kafi K M, Barau A S, Aliyu A. The effects of windstorm in African medium-sized cities: An analysis of the degree of damage using KDE hotspots and EF-scale matrix[J]. International Journal of Disaster Risk Reduction, 2021, 55:102070. doi: 10.1016/j.ijdrr.2021.102070.
doi: 10.1016/j.ijdrr.2021.102070 |
[43] |
Mohaymany A S, Shahri M, Mirbagheri B. GIS-based method for detecting high-crash-risk road segments using network kernel density estimation[J]. Geo-Spatial Information Science, 2013, 16(2):113-119.
doi: 10.1080/10095020.2013.766396 |
[44] |
Tang L L, Kan Z H, Zhang X, et al. A network kernel density estimation for linear features in space-time analysis of big trace data[J]. International Journal of Geographical Information Science, 2016, 30(9):1717-1737.
doi: 10.1080/13658816.2015.1119279 |
[45] |
Moradi M M, Rodríguez-Cortés F J, Mateu J. On kernel-based intensity estimation of spatial point patterns on linear networks[J]. Journal of Computational and Graphical Statistics, 2018, 27(2):302-311.
doi: 10.1080/10618600.2017.1360782 |
[46] |
Deng M, Yang X X, Shi Y, et al. A density-based approach for detecting network-constrained clusters in spatial point events[J]. International Journal of Geographical Information Science, 2019, 33(3):466-488.
doi: 10.1080/13658816.2018.1541177 |
[47] | Hatzakis A, Chulanov V, Gadano A C, et al. The present and future disease burden of hepatitis C virus (HCV) infections with today's treatment paradigm: Volume 2[J]. Journal of Viral Hepatitis, 2015, 22:26-45. |
[48] | Lee J, Gong J F, Li S W. Exploring spatiotemporal clusters based on extended kernel estimation methods[J]. International Journal of Geographical Information Science, 2017, 31(6):1154-1177. |
[49] |
Li M F, Shi X, Li X, et al. Sensitivity of disease cluster detection to spatial scales: An analysis with the spatial scan statistic method[J]. International Journal of Geographical Information Science, 2019, 33(11):2125-2152.
doi: 10.1080/13658816.2019.1616741 |
[50] | Laube P. The low hanging fruit is gone: Achievements and challenges of computational movement analysis[J]. SIGSPATIAL Special, 2015, 7(1):3-10. |
[51] | 李明晓, 张恒才, 仇培元, 等. 一种基于模糊长短期神经网络的移动对象轨迹预测算法[J]. 测绘学报, 2018, 47(12):1660-1669. |
[ Li Mingxiao, Zhang Hengcai, Qiu Peiyuan, et al. Predicting future locations with deep fuzzy-LSTM network. Acta Geodaetica et Cartographica Sinica, 2018, 47(12):1660-1669. ] | |
[52] | 方志祥, 倪雅倩, 黄守倩. 融合Markov与多类机器学习模型的个体出行位置预测模型[J]. 武汉大学学报(信息科学版), 2021, 46(6):799-806. |
[ Fang Zhixiang, Ni Ya-qian, Huang Shouqian. A multi-model fusion model of individual travel location prediction using Markov and machine learning methods. Geomatics and Information Science of Wuhan University, 2021, 46(6):799-806. ] | |
[53] |
Demšar U, Long J A, Benitez-Paez F, et al. Establishing the integrated science of movement: Bringing together concepts and methods from animal and human movement analysis[J]. International Journal of Geographical Information Science, 2021, 35(7):1273-1308.
doi: 10.1080/13658816.2021.1880589 |
[54] |
Katajisto J, Moilanen A. Kernel-based home range method for data with irregular sampling intervals[J]. Ecological Modelling, 2006, 194(4):405-413.
doi: 10.1016/j.ecolmodel.2005.11.001 |
[55] |
Fleming C H, Sheldon D, Fagan W F, et al. Correcting for missing and irregular data in home-range estimation[J]. Ecological Applications, 2018, 28(4):1003-1010.
doi: 10.1002/eap.2018.28.issue-4 |
[56] |
Oliver N, Lepri B, Sterly H, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle[J]. Science Advances, 2020, 6(23): eabc0764. doi: 10.1126/sciadv.abc0764.
doi: 10.1126/sciadv.abc0764 |
[57] |
Saeedimoghaddam M, Keyanpour-Rad M, Shafizadeh-Moghadam H, et al. A probabilistic space-time prism to explore changes in white Stork habitat use in Iran[J]. Ecological Indicators, 2017, 78:156-166.
doi: 10.1016/j.ecolind.2017.03.019 |
[58] |
Kie J G, Matthiopoulos J, Fieberg J, et al. The home-range concept: Are traditional estimators still relevant with modern telemetry technology?[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2010, 365:2221-2231.
doi: 10.1098/rstb.2010.0093 |
[59] | 林婉妮, 王诺. 国际海上救援效率比较研究: 以东海特定水域为例[J]. 海洋通报, 2019, 38(4):438-446. |
[ Lin Wanni, Wang Nuo. A comparison study on the rescue efficiency in the international waters: A case of the specific waters in the East China Sea. Marine Science Bulletin, 2019, 38(4):438-446. ] | |
[60] |
Downs J A, Horner M W, Tucker A D. Time-geographic density estimation for home range analysis[J]. Annals of GIS, 2011, 17(3):163-171.
doi: 10.1080/19475683.2011.602023 |
[61] | 柴彦威, 陈梓烽. 时空间行为调查的回顾与未来展望[J]. 人文地理, 2021, 36(2):3-10. |
[ Chai Yanwei, Chen Zifeng. Spspace-time behavior surveys: State-of-the-art and prospectsace-time. Human Geography, 2021, 36(2):3-10. ] |
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