Incorporating Spatial Autocorrelation with Neural Networks in Empirical Land-Use Change Models

2013 ◽  
Vol 40 (3) ◽  
pp. 384-404 ◽  
Author(s):  
Hone-Jay Chu ◽  
Chen-Fa Wu ◽  
Yu-Pin Lin
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Change Models

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

Theory, Data, and Methods

2019 ◽  
pp. 156-202
Author(s):  
Eda Ustaoglu ◽  
Arif Çagdaş Aydinoglu
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Information System ◽  
Land Use Change ◽  
Model Development ◽  
Agent Based ◽  
Change Models ◽  
Multi Scale ◽  
Land Use Dynamics ◽  
Scale Characteristics

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.

scholarly journals A Time Monte Carlo method for addressing uncertainty in land-use change models

2018 ◽  
Vol 32 (11) ◽  
pp. 2317-2333 ◽  
Author(s):  
Ahmed Mustafa ◽  
Ismaïl Saadi ◽  
Mario Cools ◽  
Jacques Teller
Keyword(s):  
Land Use ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Land Use Change ◽  
Change Models

scholarly journals Incorporating Spatial Dynamics and Temporal Dependency in Land Use Change Models

2012 ◽  
Vol 44 (4) ◽  
pp. 321-349 ◽  
Author(s):  
Raghuprasad Sidharthan ◽  
Chandra R. Bhat
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Dynamics ◽  
Change Models

scholarly journals An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)

2015 ◽  
Vol 8 (4) ◽  
pp. 3359-3402 ◽  
Author(s):  
S. Moulds ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Global Environmental Change ◽  
Model Development ◽  
Spatially Explicit ◽  
Change Models ◽  
R Programming Language ◽  
Global Environmental ◽  
R Programming ◽  
Scientific Results

Abstract. Land use change has important consequences for biodiversity and the sustainability of ecosystem services, as well as for global environmental change. Spatially explicit land use change models improve our understanding of the processes driving change and make predictions about the quantity and location of future and past change. Here we present the lulccR package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of different models; (3) different aspects of the modelling procedure must be performed in different environments because existing applications usually only perform the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the widely used CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a dataset included with the package. It is envisaged that lulccR will enable future model development and comparison within an open environment.

scholarly journals DEVELOPING AN AUTOMATED METHOD FOR THE APPLICATION OF LIDAR IN IUMAT LAND-USE MODEL: ANALYSIS OF LAND-USE CHANGES USING BUILDING-FORM PARAMETERIZATION, GIS, AND ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Vol 14 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Mohamad Farzinmoghadam ◽  
Nariman Mostafavi ◽  
Elisabeth Hamin Infield ◽  
Simi Hoque
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Artificial Neural Networks ◽  
Land Use Change ◽  
Land Use Changes ◽  
Growth Patterns ◽  
Urban Metabolism ◽  
Automated Method ◽  
Land Use Model ◽  
Building Form

Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. The study of land-use change has many implications for infrastructure design, resource allocation, and urban metabolism simulation. While most urban models focus on horizontal growth patterns, few investigate the impacts of vertical characteristics of urbanscapes in predicting land-use changes. In this paper, Building-form variables are introduced as a new determinant factor for investigating effects of vertical characteristics of an urbanscape in predicting land-use change. This work outlines an automated method for generating building-form variables from Light Detection and Ranging (LIDAR) data by using Density-Based Spatial Clustering and normal equations. This paper presents a Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict urban growth patterns within the IUMAT framework (Integrated Urban Metabolism Analysis Tool), which is an analytical platform for quantifying the overall sustainability in the urbanscape. The town of Amherst in Western Massachusetts (for the period of 1971–2005) is used as a case study for testing the model. By isolating the weights of each explanatory variable in models, this study highlights the influence of building geometry on future development scenarios.

scholarly journals A Review of Methodological Integration in Land-Use Change Models

2019 ◽  
pp. 1779-1807
Author(s):  
Anh Nguyet Dang ◽  
Akiyuki Kawasaki
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Use Planning ◽  
Essential Feature ◽  
Limited Attention ◽  
Future Trends ◽  
Change Models ◽  
Environmental Impact Assessments ◽  
Land Use Modelling ◽  
Modelling Methods

Global change research communities are paying increasing attention to answering critical questions related to land-use change, questions which are at the root of many pressing socio-economic and environmental issues. In this regard, a huge number of models have been developed to support future land-use planning and environmental impact assessments of land-use change activities. Within land-use change models, methodological integration is recognized as an essential feature for a complete model, which can help to combine the strength of single modelling methods/techniques without inherent weaknesses. Despite the potential and remarkable growth of methodological integration in land-use change models, limited attention has been paid to this aspect of integration. In response to this, the authors' paper summarizes the current major land-use modelling methods/techniques, and explains the co-integration of these methods/techniques. In addition, they summarize the achievements, limitations and future trends in the use of the methodological integration approach in land-use change models.

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