State of the Art on Artificial Intelligence in Land Use Simulation

[Abstract] This review presents a state of the art in artificial intelligence applied to urban planning and particularly to land-use predictions. In this review, different articles after the year 2016 are analyzed mostly focusing on those that are not mentioned in earlier publications. Most of the articles analyzed used a combination of Markov chains and cellular automata to predict the growth of urban areas and metropolitan regions. We noticed that most of these simulations were applied in various areas of China. An analysis of the publication of articles in the area over time is included.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (ref. ED431G/01 and ED431D 2017/16), the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002 and UNLC13-13-3503), and the European Regional Development Funds (FEDER). CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia,” supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaria Xeral de Universidades” (grant no. ED431G 2019/01)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G 2019/0

Combining a land parcel cellular automata (LP-CA) model with participatory approaches in the simulation of disruptive future scenarios of urban land use change

Urban development is a process that becomes increasingly complex as the city evolves and in which unexpected events can happen which may alter the envisaged trend over time. To anticipate and examine the sudden emergence of processes that are difficult to predict over long-term future timelines, prospective methodologies are required to manage and implement disruptive narrative storylines in future scenario planning. In this research, a method that combines Land Parcel Cellular Automata (LP-CA) and participatory approaches was developed in order to generate land use trajectories that are spatially consistent with disruptive narrative storylines. The urban-industrial corridor of Henares (Spain), which has undergone important urban transformations in recent decades, was chosen as the study area to test the model. In a preliminary validation of the LP-CA model, a Figure of Merit (FOM) value of 0.2817 indicated satisfactory performance. The results demonstrated the usefulness of the participatory scenario-building and the workshop in supporting the configuration of the model parameters and the spatial representation of complex urban dynamics. In conclusion, this methodology can be used to generate simulations of urban land use change in disruptive future scenarios and to spatially observe the propagation of the uncertainty associated with future events across different urban land uses.This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Social Fund [grant number PRE2018–084663]; the Spanish Ministry of Economy and Competitiveness [TRANSURBAN Project CSO2017–86914-C2–1-P]; and the “Estímulo a la Excelencia para Profesores Universitarios Permanentes” research programme funded by the University of Alcal´a and the Regional Government of Madrid [grant number EPU-INV/2020/009]

Analyzing and modeling the spatiotemporal dynamics of urban expansion: a case study of Hangzhou City, China

Understanding the spatiotemporal characteristics of urban expansion is increasingly important for assisting the decision making related to sustainable urban development. By integrating remote sensing (RS), spatial metrics, and the cellular automata (CA) model, this study explored the spatiotemporal dynamics of urban expansion and simulated future scenarios for Hangzhou City, China. The land cover maps (2002, 2008, and 2013) were derived from Landsat images. Moreover, the spatial metrics were applied to characterize the spatial pattern of urban land. The CA model was developed to simulate three scenarios (Business-As-Usual (BAU), Environmental Protection (EP), and Coordination Development (CD)) based on the various strategies. In addition, the scenarios were further evaluated and compared. The results indicated that Hangzhou City has experienced significant urban expansion, and the urban area has increased by 698.59 km2. Meanwhile, the spatial pattern of urban land has become more fragmented and complex. Hangzhou City will face unprecedented pressure on land use efficiency and coordination development if this historical trend continues. The CD scenario was regarded as the optimized scenario for achieving sustainable development. The findings revealed the spatiotemporal characteristics of urban expansion and provide a support for future urban development

Mapeamento temporal e predição da área da mancha urbana da região metropolitana de Porto Alegre - RS, utilizando geotecnologias e sensores remotos

A expansão urbana tem um impacto substancial na dinâmica de uso e cobertura da terra (LULC). Os mapas históricos servem como referência para realizar a análise espacial de previsão do LULC. Esta pesquisa tem como objetivo identificar a mudança do LULC na Região Metropolitana de Porto Alegre (RMPA) para os anos de 1991, 2000, 2010 e 2020 usando imagens do satélite Landsat e prever as mudanças futuras para os anos de 2030 e 2040 usando um modelo Multilayer Perceptrons (MLP) através de Artificial Neural Network (ANN). As variáveis utilizadas como entrada no modelo de previsão são as distâncias de rodovias, centros urbanos, Porto Alegre, estação ferroviária, além do relevo sombreado, declividade e os dados históricos de LULC. Os resultados apontaram que todas as variáveis têm efeitos significativos na condução da expansão urbana histórica e futura. Além disso, o índice de expansão da paisagem (LEI) foi utilizado como métrica espacial para analisar as formas de expansão urbana. Para a análise histórica do primeiro artigo foi considerado os municípios que faziam parte da RMPA em cada ano mapeado. O segundo artigo considerou os 34 municípios da RMPA para os quatro anos em estudo. Os resultados do primeiro artigo mostraram que a RMPA experimentou uma mudança inédita de LULC, aumentando sua área urbana em 336,2 km², sendo Porto Alegre e Canoas os municípios com a maior taxa de expansão. A precisão dos mapas LULC teve um excelente desempenho com coeficiente Kappa 0,85, 0,89, 0,88, e 0,91 para os anos em estudo, respectivamente. Além disso, o LEI apontou que entre 1991 e 2020 houve maior expansão na forma borda-expansão, principalmente nos municípios de Porto Alegre, Alvorada, Canoas, Cachoeirinha, Esteio, Sapucaia do Sul e São Leopoldo. Essa forma de expansão foi a mais verificada na RMPA nos últimos 30 anos, com 562 km resultando em 81%, os outros 17% foram periféricos e 2% foram de preenchimento. Esses municípios estão localizados próximos ao centro da capital da RMPA, à estação ferroviária e ao Aeroporto Internacional Salgado Filho, fatores considerados importantes para o aumento da expansão urbana. A precisão dos mapas históricos de LULC do segundo artigo apresentou um excelente desempenho com coeficiente Kappa de 0,82, 0,87, 0,86, e 0,89 para os anos em estudo, respectivamente. Os resultados históricos mostraram que entre 1991 e 2020, a classe urbanização teve um aumento de 298 km², enquanto as áreas de vegetação arbórea diminuíram um total de 322 km². O LEI no segundo artigo entre 1991 e 2020 apresentou como forma de expansão urbana predominante na RMPA a expansão de borda com cerca de 93%, principalmente nos municípios de Porto Alegre, Alvorada, Canoas, Esteio e São Leopoldo. A forma de expansão periférica e de preenchimento ocorreram em 5% e 2%, respectivamente, nos municípios mais afastados de Porto Alegre, principalmente em Capela de Santana, Montenegro, Viamão e Guaíba. Por fim, as mudanças nas previsões de modelagem futuras no LULC para os anos de 2030 e 2040 apresentaram um aumento significativo na área urbana da RMPA de 323,3 km² e 335,4 km², respectivamente. O LEI para os mapeamentos futuros apresentou resultados diferentes dos analisados historicamente, a forma expansão de borda diminuiu, mas mesmo assim continuou predominante com 79%, enquanto que a forma periféria e de preenchimento aumentaram alcançando 18% e 3%, respectivamente. Para o futuro, é previsto que as áreas urbanas aumentem mais de 650 km² até 2040 na RMPA. Esses resultados podem auxiliar os tomadores de decisão no desenvolvimento das cidades, e políticas detalhadas de gestão e planejamento urbano devem ser feitas considerando a heterogeneidade espacial e interna.Urban sprawl substantially impacts land use and land cover (LULC) dynamics. Historical maps serve as a reference to perform predictive spatial analysis. This research aims to identify the change in LULC in the Metropolitan Region of Porto Alegre (RMPA) for the years 1991, 2000, 2010, and 2020 using Landsat satellite images and predict future changes for the years 2030 and 2040 using a model Multilayer Perceptrons (MLP) through Artificial Neural Network (ANN). The variables used as input in the forecast model are the distances from highways, urban centers, Porto Alegre, and railway stations, in addition to shaded relief, slope, and historical data from LULC. The results showed that all variables significantly affect the historical and future urban expansion. In addition, the landscape expansion index (LEI) was used as a spatial metric to analyze the urban growth forms. For the first article, the municipalities were inserted accordingly to their inclusion year in the RMPA. However, in the second article, all the 34 towns in the RMPA were considered for the four years under study. The first article's results showed that the RMPA experienced an unprecedented LULC change, increasing its urban area by 336.2 km², with Porto Alegre and Canoas being the municipalities with the highest expansion rate. The LULC map’s accuracy performed excellently with Kappa coefficients of 0.85, 0.89, 0.88, and 0.91 for the four years under study, respectively. In addition, the LEI showed that between 1991 and 2020, there was a more significant expansion in the edge-expansion form, mainly in Porto Alegre, Alvorada, Canoas, Cachoeirinha, Esteio, Sapucaia do Sul and São Leopoldo municipalities. This expansion was the most verified in the RMPA in the last 30 years, with 562 km² resulting in 81% of the new urban areas, the other 17% outlying, and 2% infilling. These municipalities are close to the capital state center, the railway station, and the International Airport, factors considered important for urban sprawl increase. The LULC historical map’s accuracy from the second article also performed excellently with Kappa coefficients of 0.82, 0.87, 0.86, and 0.89 for the four years under study, respectively. The historical results showed that between 1991 and 2020, the urbanization class had an increase of 298 km², while the arboreal vegetation areas decreased by 322 km². The LEI observed between 1991 and 2020 in the second article showed that the predominant urban expansion form in the whole RMPA was edge expansion with 93%, mainly in Porto Alegre, Alvorada, Canoas, Esteio, and São Leopoldo municipalities. The outlying and infilling urban expansion form occurred in 5% and 2%, respectively, mainly far from Porto Alegre, in Capela de Santana, Montenegro, Viamão, and Guaíba municipalities. Finally, LULC's future modeling predictions for 2030 and 2040 showed a significant urban area increase of 323.3 km² and 335.4 km², respectively. The LEI for the future presented different results from those analyzed previously historically. The edge-expansion form occurs in fewer areas, but still, the majority with 79%, while the outlying and infilling increased to 18% and 3%, respectively. Therefore, in the future, urban areas are predicted to increase by more than 650 km² by 2040 in the RMPA. These results can help decision-makers in the development of cities, and detailed urban management and planning policies must be made considering spatial and internal heterogeneity

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region

Patterns of historical and future urban expansion in Nepal

Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal's Tarai region-a populous area experiencing land-use change due to urbanization and other factors-we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989-2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal's fertile Tarai region is of serious concern for food-insecure countries like Nepal

A spatial analytical approach to urbanisation

Vaz, E., Damásio, B., Bação, F., Kotha, M., Penfound, E., & Rai, S. K. (2021). Mumbai's business landscape: A spatial analytical approach to urbanisation. Heliyon, 7(7), [e07522]. https://doi.org/10.1016/j.heliyon.2021.e07522India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities. Mumbai, one of the world's largest cities, represents a unique combination of a diverse economic landscape and the growth of a megacity. The role of Mumbai in India's growth is of crucial importance for India's business landscape. This paper explores the massive urbanization processes of Mumbai's peri-urban areas and compares urban sprawl with the location of its business landscape. A spatial accounting methodology based on the proximity of Mumbai's different economic hubs will be used to measure the underlying pattern of the Mumbai region, concerning past and present urbanization, and the effect of this urbanization process has on the possible location of businesses. This business-urban ecosystem perspective will be implemented by a spatial analysis on the correlation between urban compactness and urban footprints, in relation to business concentration and its spatiotemporal evolution over the last hundred years.publishersversionpublishe

Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis

[EN] The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas.This work was supported by the the Spanish Ministerio de Economia y Competitividad and FEDER [CGL2016-80705-R].Sapena Moll, M.; Ruiz Fernández, LÁ. (2021). Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis. International Journal of Geographical Information Science. 35(2):375-396. https://doi.org/10.1080/13658816.2020.181746337539635

Predicting land use changes in northern China using logistic regression, cellular automata, and a Markov model

Abstract(#br)Land use changes are complex processes affected by both natural and human-induced driving factors. This research is focused on simulating land use changes in southern Shenyang in northern China using an integration of logistic regression, cellular automata, and a Markov model and the use of fine resolution land use data to assess potential environmental impacts and provide a scientific basis for environmental management. A set of environmental and socio-economic driving factors was used to generate transition potential maps for land use change simulations in 2010 and 2020 using logistic regression. An average relative operating characteristic value of 0.824 was obtained, indicating the validity of the logistic regression model. The logistic–cellular automata (CA)–Markov model..