【论文】
[35] Liu L, Li C, Xiao L*, Song G. Explaining Theft Using Offenders’ Activity Space Inferred from Residents’ Mobile Phone Data[J]. ISPRS International Journal of Geo-Information, 2024, 13(1): 8. https://doi.org/10.3390/ijgi13010008.
[34] Yue H, Liu L, Xiao L*. Investigating the effect of people on the street and streetscape physical environment on the location choice of street theft crime offenders using street view images and a discrete spatial choice model[J]. Applied Geography, 2023, 157: 103025. https://doi.org/10.1016/j.apgeog.2023.103025.
[33] Song G, Cai L, Liu L, Xiao L*, Wu Y, Yue H. Effects of ambient population with different income levels on the spatio-temporal pattern of theft: A study based on mobile phone big data[J]. Cities, 2023, 137: 104331. https://doi.org/10.1016/j.cities.2023.104331.
[32] 廖伊彤, 周素红, 肖露子. 不同收入群体的城市安全感地图及其环境影响因素[J]. 地理学报, 2023, 78(06): 1467-1483.
[31] 黎家琪, 宋广文, 肖露子, 等. 盗窃者犯罪出行距离的特征及其影响因素——基于居住社区、作案社区及出行物理障碍的综合考虑[J]. 地理科学进展, 2022, 41(11): 2123-2134.
[30] 宋广文, 黎晓彐, 肖露子*, 等. 交互作用视角下人口流动性与住房类型对城市入室盗窃空间格局的影响[J]. 地理研究, 2022, 41(11): 2897-2911.
[29] Zhang X, Liu L, Lan M, Song G, Xiao L, Chen J. Interpretable machine learning models for crime prediction[J]. Computers, Environment and Urban Systems, 2022, 94: 101789. https://doi.org/10.1016/j.compenvurbsys.2022.101789.
[28] Song G, Zhang C, Xiao L*, Wang Z, Chen J, Zhang X. Influence of Varied Ambient Population Distribution on Spatial Pattern of Theft from the Person: The Perspective from Activity Space[J]. ISPRS International Journal of Geo-Information, 2022, 11: 615. https://doi.org/10.3390/ijgi11120615.
[27] Zhang C, Liu L, Zhou S, Feng J, Chen J, Xiao L*. Contact-Fraud Victimization among Urban Seniors: An Analysis of Multilevel Influencing Factors[J]. ISPRS International Journal of Geo-Information, 2022, 11: 201. https://doi.org/10.3390/ijgi11030201.
[26] 柳林,刘慧婷,陈建国,肖露子,祝卫莉,孙秋远. “雷霆扫毒”对贩卖毒品犯罪的影响及后续时空分布变化——以ZG市主城区为例[J]. 地理学报, 2022, 77(06): 1461-1474.
[25] Xiao L, Ruiter S, Liu L, Song G.et al. Burglars blocked by barriers? The impact of physical and social barriers on residential burglars' target location choices in China[J]. Computers, Environment and Urban Systems, 2021, 86: 101582.
[24]柳林,孙秋远,肖露子*,宋广文,陈建国.涉毒人员日常活动对盗窃警情空间格局影响的时间差异[J].地球信息科学学报,2021,23(12):2187-2200.
[23] Xu C, Xiao L, Song G, et al. The impact of community residents’ occupational structure on the spatial distribution of different types of crimes[J]. Habitat International, 2021, 117: 102435.
[22] 柳林,吴雨菡,宋广文,肖露子.犯罪防控警务策略及其时空效益评估研究进展[J].地球信息科学学报,2021,23(01):29-42.
[21] 柳林,陈德宝,徐冲,龙冬平,肖露子,陈悉.入室盗窃临近重复案件与孤立案件分布的影响因素对比研究[J].地理科学,2021,41(09):1625-1633.DOI:10.13249/j.cnki.sgs.2021.09.014.
[20] 龙冬平,柳林,陈建国,肖露子,宋广文,徐冲.街头抢劫者前犯罪经历对其后作案地选择的影响[J].地理科学进展,2020,39(05):815-828.
[19] 张春霞,周素红,柳林,肖露子.建成环境对星级酒店内被盗的影响——以ZG市中心城区为例[J].地理科学进展,2020,39(05):829-840.
[18] Chen, J.; Liu, L.; Xiao, L.; Xu, C.; Long, D. Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model. ISPRS Int. J. Geo-Inf. 2020, 9, 60. https://doi.org/10.3390/ijgi9010060.
[17] Song G, Liu L, Bernasco W, Liu L, Xiao L, Zhou S, Liao W. Crime Feeds on Legal Activities: Daily Mobility Flows Help to Explain Thieves [J]. Journal of Quantitative Criminology, 2019:1-24. https://doi.org/10.1007/s10940-019-09406-z.
[16] Feng J, Liu L, Ren F, Xiao L. Examining the relationship between neighborhood environment and residential locations of juvenile and adult migrant burglars in China [J]. Cities, 2018,82:10-18. https://doi.org/10.1016/j.cities.2018.04.014.
[15] Song G, Liu L, Bernasco W, Xiao L, Zhou S, Liao W. Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity[J]. Journal of Annals of the American Association of Geographers, 2018(2):1-19.
[14] 肖露子,柳林,周素红,宋广文,张春霞,陈建国. ZG市工作日地铁站点扒窃案件的时空分布及其影响因素[J]. 地理科学,2018,38(8):1227-1234.
[13] 柳林,宋广文,肖露子*,周素红,宋广钦,龙冬平. 不同犯罪类型受害者报警行为特点及其影响因素分析[J]. 地理科学, 2018,38(12):1998-2005.
[12]柳林,杜方叶,宋广文,龙冬平,姜超,肖露子.犯罪共生空间的类型识别及其特征分析[J].地理科学,2018,38(08):1199-1209.DOI:10.13249/j.cnki.sgs.2018.08.001.
[11]宋江宇,周素红,柳林,龙冬平,肖露子.日常活动视角下居民健康影响的性别差异——以广州为例[J].地理科学进展,2018,37(07):999-1010.
[10] Xiao L, Liu L, Song G, Ruiter S, Zhou S. Journey-to-crime distances of residential burglars in China disentangled: Origin and destination effects [J]. ISPRS International Journal of Geo-Information, 2018,7,325.
[9]肖露子,柳林,宋广文,周素红,龙冬平,冯嘉欣. 基于理性选择理论的社区环境对入室盗窃的影响[J]. 地理研究, 2017,36(12):2479-2491.
[8]宋广文,肖露子,周素红,龙冬平,周淑丽,刘凯. 居民日常活动对扒窃警情时空格局的影响[J]. 地理学报, 2017, 72(2):356-367.
[7]柳林,杜方叶,肖露子,宋广文,刘凯,姜超. 不同类型道路密度对公共空间盗窃犯罪率的影响——基于ZG市的实证研究[J]. 人文地理, 2017, 32(6):32-46.
[6]柳林,张春霞,冯嘉欣,肖露子,贺智,周淑丽. ZG市诈骗犯罪的时空分布与影响因素[J]. 地理学报, 2017, 72(2):315-328.
[5]龙冬平,柳林,周素红,杜方叶,宋广文,肖露子.地理学视角下犯罪者行为研究进展[J].地理科学进展,2017,36(07):886-902.
[4] Chen J, Liu L, Zhou S, Xiao L, Song G, et al. Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China[J]. ISPRS International Journal of Geo-Information, 2017, 6(5):138.
[3] Chen J, Liu L, Zhou S, Xiao L, Jiang C. Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China[J]. International Journal of Geo-Information, 2017, 6(8):246.
[2]陈建国,周素红,柳林,肖露子,宋广文.交通拥堵对急救医疗服务时空可达性的影响——以广州市为例[J].地理科学进展,2016,35(04):431-439.
[1]周婷婷,肖露子,曹凯滨.增城市3维互动全景展示系统的建设与应用[J].地理信息世界,2012,10(05):60-64.
【专利\计算机软件著作权】
[4]肖露子,冯嘉欣,孙秋远,柳林。《一种巡逻路径规划方法、计算设备及存储介质》,ZL202011128625.8, 2024年4月26日授权。(专利)
[3]柳林,肖露子,周素红,李秋萍, 宋江宇,宋广文。《交通控制子区优化与自适应调整方法》, ZL201510762739.0. ,2017年10月24日授权。(专利)
[2]周素红,柳林,杨靖芸,郝新华,陈建国,肖露子。《基于路段OD反推的实时交通流分布预测系统》,ZL201410410008.5. ,2016年8月24日授权。(专利)
[1]周素红,李秋萍,柳林,肖露子,陈建国,刘凯。《浮动车GPS数据处理系统V1.0》,2016SR063248。(软件著作权)