FORMATION OF A SPATIAL DATA BASE FOR TRAINING A PREDICTIVE MODEL FOR ANALYZING THE INVESTMENT ATTRACTIVENESS OF LAND PLOTS
Rubrics: AGRICULTURAL ECONOMICS AND POLICIES
Authors:
1.
Moscow State University of Geodesy and Cartography
Type:
Article
eLocation:
Status:
Published
Received:
06.08.2023
Accepted:
15.08.2023
Published:
25.08.2023
Language:
Russian
Keywords:
investicionnaya privlekatel'nost', baza dannyh, klasterizaciya dannyh, prostranstvennyy faktor, kadastrovaya stoimost'
Abstract (English):
The research problem of predicting the investment attractiveness of land plots is not only relevant from an economic and financial point of view, but in general allows assessing the development of settlements and their districts. This article presents the experience of creating a spatial database for training a predictive model for analyzing the investment attractiveness of land plots, as well as the process of filtering anomalous values. This approach became a multi-step process that included data collection, primary analysis, and data cleansing. Through this process, a high-quality database has been created that can improve the accuracy and efficiency of predictive models in the tasks of analyzing the investment attractiveness of land plots. In addition, this experience allows using it as a basis for creating similar databases in other areas that require high-quality data for training.
The research problem of predicting the investment attractiveness of land plots is not only relevant from an economic and financial point of view, but in general allows assessing the development of settlements and their districts. This article presents the experience of creating a spatial database for training a predictive model for analyzing the investment attractiveness of land plots, as well as the process of filtering anomalous values. This approach became a multi-step process that included data collection, primary analysis, and data cleansing. Through this process, a high-quality database has been created that can improve the accuracy and efficiency of predictive models in the tasks of analyzing the investment attractiveness of land plots. In addition, this experience allows using it as a basis for creating similar databases in other areas that require high-quality data for training.
Keywords:
investicionnaya privlekatel'nost', baza dannyh, klasterizaciya dannyh, prostranstvennyy faktor, kadastrovaya stoimost'
investicionnaya privlekatel'nost', baza dannyh, klasterizaciya dannyh, prostranstvennyy faktor, kadastrovaya stoimost'
