Predviđanje promena u korišćenju zemljišta primenom modela vođenih podacima (DATA-DRIVEN MODELS)

One of the main tasks of data-driven modelling methods is to induce a representative model of underlying spatial - temporal processes using past data and data mining and machine learning approach. As relatively new methods, known to be capable of solving complex nonlinear problems, data-driven methods are insufficiently researched in the field of land use. The main objective of this dissertation is to develop a methodology for predictive urban land use change models using data-driven approach together with evaluation of the performance of different data-driven methods, which in the stage of finding patterns of land use changes use three different machine learning techniques: Decision Trees, Neural Networks and Support Vector Machines. The proposed methodology of data-driven methods was presented and special attention was paid to different data representation, data sampling and the selection of attributes by four methods (χ2, Info Gain, Gain Ratio and Correlation-based Feature Subset) that best describe the process of land use change. Additionally, a sensitivity analysis of the Support Vector Machines -based models was performed with regards to attribute selection and parameter changes. Development and evaluation of the methodology was performed using data on three Belgrade municipalities (Zemun, New Belgrade and Surčin), which are represented as 10×10 m grid cells in four different moments in time (2001, 2003, 2007 and 2010). The obtained results indicate that the proposed data-driven methodology provides predictive models which could be successfully used for creation of possible scenarios of urban land use changes in the future. All three examined machine learning techniques are suitable for modeling land use change. Accuracy and performance of models can be improved using proposed balanced data sampling, including the information about neighbourhood and history in data representations and relevant attribute selections. Additionally, using selected subset of attributes resulted in a simple model and with less possibility to be overfitted with higher values of Support Vector Machines parameters.Један од главних задатака моделирања метода вођених подацима (Data-driven methods) је проналажење репрезентативног модела испитивног просторно временског процеса, применом података из прошлости и Data Mining и Machine Learning приступа..

Simulation of land use changes using cellular automata and artificial neural network

This paper presents a method integrating artificial neural network (ANN) in cellular automata (CA) to simulate land use changes in Luxembourg and the areas adjacent to its borders. The ANN is used as a base of CA model transition rule. The proposed method shows promising results for prediction of land use over time. The ANN is validated using cross-validation technique and Receiver Operating Characteristic (ROC) curve analysis, and compared with logit model and a support vector machine approach. The application described in this paper highlights the interest of integrating ANNs in CA based model for land use dynamic simulation.Artificial neural network; Cellular automata; Modelling; Land use changes; Spatial planning and dynamics

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Data visualization within urban models

Models of urban environments have many uses for town planning, pre-visualization of new building work and utility service planning. Many of these models are three-dimensional, and increasingly there is a move towards real-time presentation of such large models. In this paper we present an algorithm for generating consistent 3D models from a combination of data sources, including Ordnance Survey ground plans, aerial photography and laser height data. Although there have been several demonstrations of automatic generation of building models from 2D vector map data, in this paper we present a very robust solution that generates models that are suitable for real-time presentation. We then demonstrate a novel pollution visualization that uses these models