{"title":"TOWARDS THE INVESTMENT POTENTIAL ASSESSMENT USING SPATIAL DATA MULTI-CRITERIA ANALYSIS AND LINEAR REGRESSION","authors":"Josip Lisjak, H. Tomić, Ante Rončević, M. Roić","doi":"10.5593/sgem2022/2.1/s08.19","DOIUrl":null,"url":null,"abstract":"The paper presents the results of research on the possibility of calculating the investment potential of a particular area based on its spatial characteristics. The level of spatial unit in this research is local administrative unit (cities or municipalities), while the geographic coverage is entire area of Republic of Croatia. Regarding the method, the results could be applied internationally and are not limited to national borders. Furthermore, when deciding on investing, it is important to know the risk. This risk in the pre-investment cycle is generally estimated on the basis of well-known wellestablished economic methods - without applying multiple criteria in the potential assessment and, among others, criteria of spatial characteristics as one of the most influential ones. Therefore, there was a need to model the investment potential as a precondition for risk calculations based on spatial criteria, which was carried out through this research using multi-criteria GIS analysis. The research in this paper is focused on testing the correlation of spatial features of certain local unit with its development index. The source data used are existing spatial data in the National Spatial Data Infrastructure (NSDI) platform, open data, and the development index as a composite index. The paper shows the results of OLS method and conclusions about influence from certain spatial characteristics on development index, and accordingly the location investment potential based on the results can be modelled.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5593/sgem2022/2.1/s08.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper presents the results of research on the possibility of calculating the investment potential of a particular area based on its spatial characteristics. The level of spatial unit in this research is local administrative unit (cities or municipalities), while the geographic coverage is entire area of Republic of Croatia. Regarding the method, the results could be applied internationally and are not limited to national borders. Furthermore, when deciding on investing, it is important to know the risk. This risk in the pre-investment cycle is generally estimated on the basis of well-known wellestablished economic methods - without applying multiple criteria in the potential assessment and, among others, criteria of spatial characteristics as one of the most influential ones. Therefore, there was a need to model the investment potential as a precondition for risk calculations based on spatial criteria, which was carried out through this research using multi-criteria GIS analysis. The research in this paper is focused on testing the correlation of spatial features of certain local unit with its development index. The source data used are existing spatial data in the National Spatial Data Infrastructure (NSDI) platform, open data, and the development index as a composite index. The paper shows the results of OLS method and conclusions about influence from certain spatial characteristics on development index, and accordingly the location investment potential based on the results can be modelled.