Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN) models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM) obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR) is equal to 88.91%. Percent Correct Match (PCM) and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively

Conceptual modeling of real property objects for the hellenic cadastre

One of the most important factors in urban planning is spatial structuring of physical plan of residential areas with respect to the day to day increasing population that leads to urban expansion horizontally and vertically. Land Information Systems (LIS) is a combination of technical and managerial aspects to optimize geospatial data collection and management to be used as a spatial decision support systems especially for urban managers and decision makers

The Issue of Uncertainty Propagation in Spatial Decision Making

Abstract GISs give users facilities to integrate and analyze data from different sources with different scale, accuracy, resolution and quality of the original data which are the key aspects of GIS functionality, but it does raise the question as to what effects the combination of different levels of data uncertainty has on both the output maps and on the data derived from spatial query and analysis. In this paper, in addition to provide an overview of uncertainty propagation assessment in overlay analysis, an experiment using Monte Carlo simulation method has been performed and then the results were analyzed. Two polygons whose vertices have been perturbed by changing their coordinates randomly using Monte Carlo simulation method are overlaid so that their intersection defines the third polygons set which in turn were statistically analyzed using a developed program and some GPS data. Two mainly recommended indicators, i.e., area and perimeter of polygon, were used and ended up with consequence that the indices of these polygons whose vertices had error in position emerged less than those whose vertices were accurate.