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

Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change

Doctor of Philosophy

dissertationThis research focuses on the application of geographic information systems (GIS) and spatial analysis methods to urban and regional development studies. GIS-based spatial modeling approaches have recently been used in examining regional development disparities and urban growth. Through the cases of Guangdong province and the city of Dongguan, the study employs a spatial-temporal, multiscale, and multimethodology approach in analyzing geographically referenced socioeconomic and remote sensing data. A general spatial data analysis framework is set through a study of regional development in China's Guangdong province and urban growth in the city of Dongguan. Three intensive spatial statistical analyses are carried out. First, the dissertation investigates the spatial dynamics of regional inequality through Markov chains and spatial Markov-chain analyses. In so doing, it addresses the effect of self-reinforcing agglomeration on regional disparities. Multilevel modeling is further employed to evaluate the relative importance of regional development mechanisms in Guangdong. Second, a spatial filtering perspective is employed for understanding the spatial effects on multiscalar characteristics of regional inequality in Guangdong. Spatial panel and space-time regression models are integrated to detail the spatial and temporal heterogeneity of underlying mechanisms behind regional inequality. Third, drawing upon a set of high-quality remote sensing data in the city of Dongguan, the dissertation analyzes the spatial-temporal dynamics and spatial determinants of urban growth in a rapid industrializing area. Through the application of landscape metrics, three types of urban growth, including infill, spontaneous, and edge expansion, are distinguished, addressing the diverse spatial patterns at different stages of urban growth. A spatial logistic approach is further developed to model the spatial variations of urban growth determinants within the Dongguan city. In short, the dissertation finds that regional inequality in the Guangdong province is sensitive to spatial scales, dependence, and the core-periphery structure therein. The evolution of inequality can hardly be simplified into either convergence or divergence trajectories. Furthermore, development mechanisms and urban growth determinants are apparently different in space and are sensitive to spatial hierarchies and regimes. Overall, through the application of GIS spatial modeling techniques, the dissertation has provided more valuable information about spatial effects on China's urban and regional development under economic transition and highlights the importance of taking into consideration spatial dimensions in urban and regional development studies

PENGARUH DINAMIKA LAHAN URBAN TERHADAP SEBARAN KEKRITISAN DAERAH RESAPAN PADA DAERAH ALIRAN SUNGAI YANG BERMUARA DI TELUK JAKARTA

Jakarta sebagai pusat pemerintahan dan perekonomian Indonesia, telah menjadi tujuan utama urbanisasi selama bertahun-tahun. Kondisi tersebut mendorong terjadinya perubahan tutupan lahan yang masif, terutama pertumbuhan lahan urban pada wilayah Jakarta dan peri urban Jakarta. Pertumbuhan lahan urban yang tidak terkendali memberikan dampak negatif bagi kota Jakarta, mengingat secara geografis kota Jakarta merupakan wilayah hilir dari delapan Daerah Aliran Sungai (DAS). Penelitian ini memiliki lima tujuan, yang pertama menganalisis tren pertumbuhan lahan urban pada seluruh DAS yang bermuara di Teluk Jakarta. Tahap analisis ini dilakukan dengan melihat tipe pertumbuhan lahan urban (infilling, edge expansion dan outlying), pola pertumbuhan lahan urban melalui analisis spasial metrik dan arah pertumbuhan lahan urban melalui pembagian delapan zona sesuai arah mata angin. Hasilnya menunjukkan bahwa tipe pertumbuhan lahan urban pada areal studi didominasi tipe Edge Expansion. Pola pertumbuhan lahan urban cenderung semakin kompak dengan tingkat sprawl yang cenderung menurun. Selain itu pertumbuhan lahan urban di wilayah Barat Laut (NW) cenderung lebih teratur dibanding wilayah lain, sedangkan wilayah Timur (E) cenderung sebaliknya. Tujuan kedua adalah menganalisis faktor pendorong dan penghambat pertumbuhan lahan urban melalui studi referensi dan wawancara mendalam. Hasilnya diperoleh tiga belas faktor pendorong dan enam faktor penghambat pertumbuhan lahan urban. Lahan urban eksisting menjadi faktor pendorong utama pertumbuhan lahan urban pada areal studi. Tujuan ketiga adalah memprediksi pertumbuhan lahan urban tahun 2029 dengan model cellular automata melalui dua skenario yaitu skenario tren pertumbuhan lahan urban (skenario 1) dan skenario rencana pengembanan wilayah (skenario 2). Hasilnya, prediksi pertumbuhan lahan urban tahun 2029 untuk skenario 1 dan skenario 2 tidak menunjukkan perbedaan yang signifikan. DAS Bekasi, DAS Angke Pesanggrahan dan DAS Cisadane diprediksi menjadi DAS dengan pertumbuhan lahan urban terbesar tahun 2029. Tujuan keempat adalah menganalisis kekritisan daerah resapan mengacu pada Penyusunan Rencana Teknik Rehabilitasi Hutan dan Lahan Daerah Aliran Sungai. Hasilnya menunjukkan bahwa pertumbuhan lahan urban tahun 2001 - 2017 maupun tahun 2017 - prediksi tahun 2029 (skenario 1 dan 2) telah menambah secara signifikan luas daerah resapan dengan tingkatan agak kritis dan kritis. Tujuan yang kelima adalah menyusun strategi pengendalian pertumbuhan lahan urban pada areal studi melalui analisis SWOT. Hasil analisis merekomendasikan upaya pengendalian pertumbuhan lahan urban dengan strategi konservatif. Srategi yang dapat diterapkan berupa pembuatan sistem informasi berbasis internet (komputer) yang mampu menjadi jembatan koordinasi sekaligus media pengawasan serta penguatan peran BKSP Jabodetabekpunjur dalam pengendalian pertumbuhan lahan urban. Kata Kunci : Dinamika Lahan Urban, Kekritisan Daerah Resapan, Daerah Aliran Sungai, cellular automata, Teluk Jakarta, analisis SWOT Jakarta as the center of Indonesia's economy and govenment, has become the main destination for urbanization for many years. This condition encourages massive land cover changes, especially urban growth in Jakarta and peri-urban areas of Jakarta. Uncontrolled urban growth has negative impacts on Jakarta, considering that geographically Jakarta is a downstream area of eight watersheds. This study has five objectives, the first to analyze urban growth trends in all watersheds that flow into the Jakarta Bay. This analysis is carried out through studies of urban growth types (infilling, edge expansion and outlying), urban growth patterns through spatial metric analysis and urban growth direction by dividing the study area into eight zones. The results show that the type of urban growth in the study area is dominated by the Edge Expansion type. Urban growth patterns tend to be more compact with sprawl rates that tend to decline. In addition, urban growth in the Northwest region (NW) tends to be more regular than other regions, while the Eastern region (E) tends to be the opposite. The second objective is to analyze the driving factors and inhibiting factors of urban growth through reference studies and in-depth interviews. The results obtained thirteen driving factors and six inhibiting factors of urban growth. Existing urban areas were the main driving factor for urban growth in the study area. The third objective is to predict urban growth in 2029 with cellular automata models through two scenarios, namely scenario of urban growth trends (scenario 1) and scenario of regional development plans (scenario 2). The result show that predictions of urban growth in 2029 for scenario 1 and scenario 2 did not show a significant difference. The Bekasi watershed, Angke Pesanggrahan watershed and the Cisadane watershed are predicted to experience the largest urban growth in 2029. The fourth objective is to analyze the criticality level of the catchment area referring to Planning Procedures for Forest and Land Rehabilitation Engineering Watershed. The results show that urban growth from 2001 to 2017 and 2017 to the prediction of 2029 have significantly increased catchment areas with rather critical and critical levels. The fifth objective is to develop a strategy for controlling urban growth in the study area through a SWOT analysis. The results of the analysis recommend controlling the urban growth with a conservative strategy. The strategy that can be applied is the creation of an internet-based information system (computer) that is able to become a bridge of coordination as well as a media of supervision. In addition, it is necessary to strengthen the role of the BKSP Jabodetabekpunjur in controlling urban growth. Keywords: Urban dynamics, criticality level of the cactment area, watershed, cellular automata, Jakarta Bay, SWOT analysi

Spatiotemporal model for landscape ecological assessment in landscape planning

Ecological landscape area is rich with biodiversity and ecosystem are two important factors that balance the serenity of the environment through its ecological function and services. However, landscape change especially rapid urbanization has led to extensive land use and land cover (LULC) transformation that degrades the ecological landscape area and ecosystem services. The limitation of integration analysis in LULC change with ecological interaction has caused detrimental impact on natural landscape area and environmental quality. Analysing the spatiotemporal characteristics of landscape changes and ecological response in a multidisciplinary research is necessary to extend the understanding of spatial change behaviour and ecological consequences. Thus, the aim of this research is to study the integration of spatiotemporal dimension of landscape change with ecological landscape sensitivity consideration in Iskandar Malaysia region (Johor Bahru). The spatiotemporal dimension of historical and future LULC change is analysed to identify the direction and characteristics of the landscape structure and function change. Logistic regression model, analytical hierarchical process, markov chain model and cellular automata were used to identify the spatiotemporal LULC change in the study area. A series of landscape matrices in landscape index at class and landscape levels were used to analyse the spatiotemporal dimension of the landscape change pattern. It includes measurement of the ecological integrity and function responses towards spatiotemporal landscape change by using Core Area Model. Satellite images of 1994, 2000, 2007 and 2013 were used to understand the historical landscape changes and as a basis for future projection. Geographic Information System and Remote Sensing were utilized to evaluate the temporal landscape characteristics and spatial pattern changes. The results indicate that rapid urbanization of Iskandar Malaysia region from 2007 to 2013 has substantially changed the structure and function of the ecological area. The urban area significantly increased from 8,031.6 hectares (3.84%) in 1994 to 42,972.94 (20.1%) in 2013, and expected to increase to 112,224.6 hectares (53.59%) in 2030. As a consequence, the natural ecological areas reduced from 55,201.77 hectares (26.37%) in 1994 to 19,011.5 hectares (9.08%) in 2013. Due to the landscape mosaic change, the core ecological areas are affected from 21,465.9 hectares (38%) reduced to 9,317.61 hectares (49%) and expected to further reduce at 8,416.71 hectares (41%) in 1994, 2013 and 2030, respectively. It shows the response of ecological condition in natural landscape areas towards the landscape changes which subsequently disturb the ecological values and services. As a conclusion, the findings of this research could provide decision makers with better understanding on the environmental consequences of the landscape changes. In addition, it contributes to enhancement of methods in multidisciplinary research and finally increases the capability of the process in adaptive management for the spatiotemporal landscape change

Land Use Land Cover (LULC) Dynamics by CA-ANN and CA-Markov Model Approaches: A Case Study of Ranipet Town, India

The present study analyzed the spatio-temporal variations in the Land Use Land Cover types within Ranipet Municipal town in Ranipet District, Tamil Nadu State, India, using two different platforms (QGIS and IDRISI Selva v.17.0). The possible parameters driven the net changes in the Land Use Land Cover (LULC) types were also incorporated for the analysis. Results revealed the positive net changes in the built-up area are about 26.8%, and combined other classes like vegetation, barren land, and water bodies have net negative changes during 1997-2019. Particularly barren land was found to have a reduction of 17.4% due to the massive industrialization in the study area. Further, the LULC maps were used for future prediction (2029) using the dynamic models of CA-ANN (Cellular Automata and Artificial Neural Network) and CA-Markov. Predicted maps yielded a kappa index of 81.6% and 82.6% for CA-ANN and CA-Markov, representing their respective accuracy levels. The CA-Markov model is extended for determining the probable long-term changes for 2080 in LULC with a kappa index of 76.2%. Compared to the CA-ANN model using the QGIS platform, CA-Markov provided better analysis, particularly from one cell to the other. According to the survey and the ground truth in the locality, industrialization and occupational shift were the most influential drivers of LULC dynamics. Moreover, the results of this study assist the stakeholders in the decision-making process for future sustainable land use management

Land Change Science and the STEPLand Framework : An Assessment of Its Progress

This contribution assesses a new term that is proposed to be established within Land Change Science: Spatio-TEmporal Patterns of Land ('STEPLand'). It refers to a specific workflow for analyzing land-use/land cover (LUC) patterns, identifying and modeling driving forces of LUC changes, assessing socio-environmental consequences, and contributing to defining future scenarios of land transformations. In this article, we define this framework based on a comprehensive meta-analysis of 250 selected articles published in international scientific journals from 2000 to 2019. The empirical results demonstrate that STEPLand is a consolidated protocol applied globally, and the large diversity of journals, disciplines, and countries involved shows that it is becoming ubiquitous. In this paper, the main characteristics of STEPLand are provided and discussed, demonstrating that the operational procedure can facilitate the interaction among researchers from different fields, and communication between researchers and policy makers

Geosimulation and Multicriteria Modelling of Residential Land Development in the City of Tehran: A Comparative Analysis of Global and Local Models

Conventional models for simulating land-use patterns are insufficient in addressing complex dynamics of urban systems. A new generation of urban models, inspired by research on cellular automata and multi-agent systems, has been proposed to address the drawbacks of conventional modelling. This new generation of urban models is called geosimulation. Geosimulation attempts to model macro-scale patterns using micro-scale urban entities such as vehicles, homeowners, and households. The urban entities are represented by agents in the geosimulation modelling. Each type of agents has different preferences and priorities and shows different behaviours. In the land-use modelling context, the behaviour of agents is their ability to evaluate the suitability of parcels of land using a number of factors (criteria and constraints), and choose the best land(s) for a specific purpose. Multicriteria analysis provides a set of methods and procedures that can be used in the geosimulation modelling to describe the behaviours of agents. There are three main objectives of this research. First, a framework for integrating multicriteria models into geosimulation procedures is developed to simulate residential development in the City of Tehran. Specifically, the local form of multicriteria models is used as a method for modelling agents’ behaviours. Second, the framework is tested in the context of residential land development in Tehran between 1996 and 2006. The empirical research is focused on identifying the spatial patterns of land suitability for residential development taking into account the preferences of three groups of actors (agents): households, developers, and local authorities. Third, a comparative analysis of the results of the geosimulation-multicriteria models is performed. A number of global and local geosimulation-multicriteria models (scenarios) of residential development in Tehran are defined and then the results obtained by the scenarios are evaluated and examined. The output of each geosimulation-multicriteria model is compared to the results of other models and to the actual pattern of land-use in Tehran. The analysis is focused on comparing the results of the local and global geosimulation-multicriteria models. Accuracy measures and spatial metrics are used in the comparative analysis. The results suggest that, in general, the local geosimulation-multicriteria models perform better than the global methods

Ecological Security Analysis of Land Use Changes in Lavasanat Basin Using Landscape Metrics

Continuous urbanization over the past decades has caused a large concentration of human population in these areas. Due to the rapid growth of the population and the rapid development of urban disorder in Iran, changes in land use and land cover are occurring rapidly and the sustainability of cities is decreasing day by day. Therefore, understanding the effects of urban growth on the ecosystem and determining the relationship between urban dynamics and ecological security are vital for effective urban planning and environmental protection, to support and support sustainable development.The purpose of this study was to monitor and predict land use changes over a 4 year period (2040-2000) with the Markov Chain Model (CA-Markov) in the Lavasanat Basin of Tehran Province and to evaluate the ecological security of this area over time periods. Landsat satellite imagery was used to investigate land use changes. According to the existing land use in the area, five land uses were considered, barren land, pasture land, irrigated land and agricultural and agricultural land. To quantify the landscape patterns in class metrics of NP, LSI, IJI, CA, PLAND and LPI. And NP, LSI, IJI, ED, PD and SPILT metrics were calculated on the landscape surface.Forecasting results for 2040 shows that at each floor level, the number of spots other than the Bayer floor will decrease with the current trend