Spatial Data Analysis and Modeling, Geoprocessing to Support Real Estate Decisions on Spatial Data of Tirana

Spatial Modeling of Censored Survival Data

An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored survival data is one of these sources that are rarely considered in the literatures. In this paper,...

Bayesian Prediction in Spatial Data Analysis

Bayesian Analysis of Survival Data with Spatial Correlation

Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study‎. ‎One of the most important issues in the analysis of survival data with spatial dependence‎, ‎is estimation of the parameters and prediction of the unknown values in known sites based on observations vector‎. ‎In this paper to analyze this type of survival‎, ‎Cox...

Determination of Optimal Sampling Design for Spatial Data Analysis

Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]

Development of Spatial Decision Support Systems for Residential Real Estate

Geographic Information Systems (GIS) can enhance the efficiency and effectiveness of decision making in the residential real estate industry. They can organize, manage, and analyze information in ways that were not possible with traditional information management systems. Although GIS are now used to perform specific business functions, their use can be magnified and extended through the creati...