AbstractsMathematics

In the world of globalization immigration processes represent consequence of the search for better life. Every year more immigrants are coming to stay and live in Finland. Understanding patterns of living, spatial locations and clustering of this specific population becomes important and integral step towards integration of immigration population in society. Studies of immigration population conducted in Finland and Helsinki Metropolitan Area are mostly done with descriptive statistical methods mostly employed for describing social patterns and participation of immigrant population within the whole population. Employment of inferential statistical methods, spatial statistical methods, precisely Exploratory Spatial Data Analysis (ESDA methods), specifically Global and Local Moran's Index is becoming extremely important because of the quantitative and qualitative results which can be gained. This thesis is consisted of analysis of immigrant population patterns, conducted by Global and Local Moran's Index used by ArcGIS and GeoDa software. ArcGIS is a market leading, commercial GIS package for computation, analysis and production of different sorts of GIS analysis and results. Spatial statistic toolbox, as integral part of ArcGIS software package is used for interpretation of spatial statistics results (maps, graphs, reports etc.), which can be obtained, by use of several different methods. GeoDa is non-commercial software, relatively new in GIS practice in Finland, focusing specifically in spatial statistics analysis. It is used for manipulation and operationalization of spatial data analysis, designed for implementation of different and unique (Bivariate Moran’s I, etc.) ESDA techniques. Both software are computing comparable but different results, quantitatively and visually. For global measurements of spatial autocorrelation and presence of clustering within analyzed area Global Moran’s Index is employed. Local measurements and for mapping of possible cluster and outlier occurrences (Anselin Local Moran’s Index) is being used. Employment of weight matrix produced in ArcGIS and GeoDa is allowing creation of conceptualization of spatial weight matrix on the same principles in ArcGIS and GeoDa. Conceptualization of weight matrix in the case of lattice data with shared border is contiguity concept. Contiguity concept is using queen concept for defining neighbors, because it allows bigger analyzing capacity. Both software are using same statistical equations but outcome results are showing variety of differences, because of the differences in computing, presenting and visual displaying of the results. GeoDa is producing more significant statistical and visual results. The task is to test and compare computational, visual and analytical capabilities and possibilities of both software and analyze quality of outcome results (maps, diagrams, box plots, etc.) Data on immigration population is provided by HSY (Helsingin Seudun Ympäristö) with the lattice grid level size (1x1km, 500x500m, 250x250m). Purpose of my thesis…