The article presents the analysis of the current situation of the forest area in Alytus and Vilnius Counties. Comparative, analytical as well as statistical and logical analysis methods were used for the investigation. The aim of the investigation is to carry out the analysis of the Alytus and Vilnius Counties forest area during the period between the years 2006 and 2018. The object of the investigation – Alytus and Vilnius Counties forest area. Tasks of the investigation: 1. To describe the status quo of forest in Alytus and Vilnius Counties. 2. To analyze and compare the forest area change in Alytus and Vilnius counties during the period between the years 2006 and 2018. The study found that forests prevailing in Alytus and Vilnius Counties are 50–59 years old. It was determined that pine trees prevail in Alytus County (71.05 percent) and in Vilnius County (16.38 percent) as well. The type of ownership prevailing in both Alytus and Vilnius counties is the forests of state significance managed by forest enterprises, national parks and state reserves. In Alytus County, during the period between the years 2006 and 2018, the forest area decreased by 4123.16 ha or 1.55 percent, in Vilnius County increased by 9593.16 ha or 2,35 percent.

The main purpose of data mining in private and public sector institutions is to process and analyse data with the aim of generating reliable information for decision-making. Decision-making performance is determined by the availability of the relevant data and the user’s ability to adapt that data for analytical purposes. The popularity of spatial statistical tools is on the rise owing to the complexity of the analysed factors, their variation over time and their correlations with the spatial structure. Popular models should be applied in demographic analyses for the needs of the spatial planning process. The availability of high-resolution data and accurate analytical tools enhances the value of spatial analyses, and the described models can be universally applied to support the decision-making process. The aim of this study was to present the applicability of selected spatial statistical models for analysing demographic data in the planning process and to identify the main advantages of these models.