[1] YUAN Y, ZHAO T, WANG W, et al. Projection of the spatially explicit land use/cover changes in China, 2010-2100[J]. Advances in meteorology, 2013(2/3): 1-9.
[2] ALMEIDA C M, GLERIANI J M, CASTEJON E F, et al. Using neural networks and cellular automata for modelling intra-urban land-use dynamics[J]. International journal of geographical information science: 2008, 22(9): 943-963.
[3] DENG J S, WANG K, HONG Y, et al. Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization[J]. Landscape and urban planning, 2009, 92(3/4): 187-198.
[4] DADASHPOOR H, AZIZI P, MOGHADASI M. Land use change, urbanization, and change in landscape pattern in a metropolitan area[J]. Science of the total environment, 2019, 655: 707-719.
[5] GU W, GUO J, FAN K, et al. Dynamic land use change and sustainable urban development in a third-tier city within Yangtze Delta[J]. Procedia environmental sciences, 2016, 36: 98-105.
[6] 王家丰, 王蓉, 冯永玖, 等. 顾及轨道交通影响的浙中城市群土地利用多情景模拟与分析[J]. 地球信息科学学报, 2020, 22(3): 605-615.
[7] BATTY M. Urban evolution on the desktop: simulation with the use of extended cellular automata[J]. Environment and planning a, 1998, 30(11): 1943-1967.
[8] LI X, CHEN Y, LIU X, et al. Experiences and issues of using cellular automata for assisting urban and regional planning in China[J]. International journal of geographical information science, 2017, 31(8): 1606-1629.
[9] LI X, YEH A G. Neural-network-based cellular automata for simulating multiple land use changes using GIS[J]. International journal of geographical information science, 2002, 16(4): 323-343.
[10] LIU X, MA L, LI X, et al. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata[J]. International journal of geographical information science, 2014, 28(1): 148-163.
[11] LIU X, LIANG X, LI X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and urban planning, 2017, 168: 94-116.
[12] BARREIRA-GONZáLEZ P, GóMEZ-DELGADO M, AGUILERABENAVENTE F. From raster to vector cellular automata models: a newapproach to simulate urban growth with the help of graph theory[J]. Computers, environment and urban systems, 2015, 54: 119-131.
[13] STEVENS D, DRAGI?EVI? S. A GIS-based irregular cellular automata model of land-use change[J]. Environment and planning b: planning and design, 2007, 34(4): 708-724.
[14] O’SULLIVAN D. Exploring spatial process dynamics using irregular cellular automaton models[J]. Geographical analysis, 2001, 33(1): 1-18.
[15] MORENO N, MéNARD A, MARCEAU D J. VecGCA: a vector-based geographic cellular automata model allowing geometric transformations of objects[J]. Environment and planning b: planning and design, 2008, 35(4): 647-665.
[16] SEMBOLONI F. The growth of an urban cluster into a dynamic selfmodifying spatial pattern[J]. Environment and planning b, planning and design, 2000, 27(4): 549-564.
[17] NORTE PINTO N, PAIS ANTUNES A. A cellular automata model based on irregular cells: application to small urban areas[J]. Environment and planning b: planning and design, 2010, 37(6): 1095-1114.
[18] YAO Y, LIU X, LI X, et al. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata[J]. International journal of geographical information science, 2017, 31(12): 2452-2479.
[19] JJUMBA A, DRAGI?EVI? S. High resolution urban land-use change modeling: Agent iCity approach[J]. Applied spatial analysis and policy, 2012, 5(4): 291-315.
[20] 于茜, 白建军, 张晶言, 等. 路网通达性与城镇空间扩展的耦合关系——以西安市为例[J]. 经济地理, 2016, 36(10): 69-75.
[21] HE L, LIU Y, H Q, et al. Simulating urban cooperative expansion in a single-core metropolitan region based on improved CA model integrated information flow: case study of Wuhan urban agglomeration in China[J]. Journal of urban planning and development, 2018, 144(2): 05018002.
[22] YU Y, HE J, TANG W, et al. Modeling urban collaborative growth dynamics using a multiscale simulation model for the Wuhan urban agglomeration area, China[J]. ISPRS international journal of geo-information, 2018, 7(5): 176.
[23] FOTHERINGHAM A S. Spatial structure and distance-decay parameters[J]. Annals of the Association of American Geographers, 1981, 71(3): 425-436.
[24] 张童, 姚士谋, 胡伟平, 等. 基于交通可达性的广佛都市区城市扩展的模拟与分析[J]. 地理科学, 2018, 38(5): 737-746.
[25] ZHU J, ZHENG J, DI S, et al. Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: a case in Nanjing, China[J]. ISPRS international journal of geo-information, 2021, 10(5): 308.
[26] ALJOUFIE M, ZUIDGEEST M, BRUSSEL M, et al. A cellular automatabased land use and transport interaction model applied to Jeddah, Saudi Arabia[J]. Landscape and urban planning, 2013, 112: 89-99.
[27] HE C, ZHAO Y, TIAN J, et al. Modeling the urban landscape dynamics in a megalopolitan cluster area by incorporating a gravitational field model with cellular automata[J]. Landscape and urban planning, 2013, 113: 78-89.
[28] LIANG S. Research on the urban inf luence domains in China[J]. International journal of geographical information science, 2009, 23(12): 1527-1539.
[29] JINGHU P, WEISHENG L. Quantitative delimitation of urban influential hinterland in China[J]. Journal of urban planning and development, 2015, 141(4): 04014033.
[30] LV J, WANG Y, LIANG X, et al. Simulating urban expansion by incorporating an integrated gravitational field model into a demand-driven random forest-cellular automata model[J]. Cities, 2021, 109: 103044.
[31] XIAO Y, WANG F, LIU Y, et al. Reconstructing gravitational attractions of major cities in China from air passenger flow data, 2001-2008: a particle swarm optimization approach[J]. The professional geographer, 2013, 65(2): 265-282.
[32] WOLD S, ESBENSEN K, GELADI P. Principal component analysis[J]. Chemometrics and intelligent laboratory systems, 1987, 2(1/3): 37-52.
[33] STOICA I, TULLA A F, ZAMFIR D, et al. Exploring the urban strength of small towns in Romania[J]. Social indicators research, 2020, 152(3): 843-875.
[34] TOBLER W R. A computer movie simulating urban growth in the Detroit region[J]. Economic geography, 1970, 46(Supplement 1): 234-240.
[35] PINTO N, ANTUNES A O N P, ROCA J. A cellular automata model for integrated simulation of land use and transport interactions[J]. ISPRS international journal of geo-information, 2021, 10(3): 149.
[36] NAVARRO CERRILLO R M, PALACIOS RODR I GUEZ G, CLAVERO RUMBAO I, et al. Modeling major rural land-use changes using the GISbased cellular automata metronamica model: the case of Andalusia (Southern Spain)[J]. ISPRS international journal of geo-information, 2020, 9(7): 458.
[37] ZHAO L, SHEN L. The impacts of rail transit on future urban land use development: a case study in Wuhan, China[J]. Transport policy, 2019, 81: 396-405.
[38] PONTIUS R G, BOERSMA W, CASTELLA J, et al. Comparing the input, output, and validation maps for several models of land change[J]. The annals of regional science, 2008, 42(1): 11-37.
[39] ZHUANG H, LIU X, YAN Y, et al. Integrating a deep forest algorithm with vector-based cellular automata for urban land change simulation[J]. Transactions in GIS, 26, 2056-2080. https://doi.org/10.1111/tgis.12935.
[40] ZHAI Y, YAO Y, GUAN Q, et al. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata[J]. International journal of geographical information science, 2020, 34(7): 1475-1499.
[41] HEUVELINK G B. Error propagation in environmental modelling with GIS[M]. New York: CRC Press, 1998.
[42] FENG Y, TONG X. A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods[J]. International journal of geographical information science, 2020, 34(1): 74-97.
[43] YAO Y, LI X, LIU X, et al. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model[J]. International journal of geographical information science, 2016, 31(4): 825-848.
[44] YAO Y, LIU X, LI X, et al. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata[J]. International journal of geographical information science, 2017, 31(12): 2452-2479.
[45] CHEN J, CHANG K, KARACSONYI D, et al. Comparing urban land expansion and its driving factors in Shenzhen and Dongguan, China[J]. Habitat international, 2014, 43: 61-71.
[46] WAND M P, JONES M C. Kernel smoothing[M]. New York: CRC Press,1994.
[47] YUAN J, ZHENG Y, XIE X. Discovering regions of different functions in a city using human mobility and POIs(DRoF)[C] // The 18th SIGKDD conference on knowledge discovery and data mining. ACM, 2012.
[48] LI S, ZHUANG C, TAN Z, et al. Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China[J]. Journal of transport geography, 2021, 91: 102974.
[49] LIU Y, WANG F, KANG C, et al. Analyzing relatedness by Toponym Co-Occurrences on web pages[J]. Transactions in GIS, 2014, 18(1): 89-107.