In Bosnia and Herzegovina, there is a significant lack of research combining Artificial Intelligence (AI), Business Intelligence (BI), and geospatial analysis in tourism planning. This paper aims to fill that gap by demonstrating the integration of these technologies applied to spatial data in order to analyze tourist movement patterns. Spatial data processing was performed using QGIS (Quantum Geographic Information System), an open-source Geographic Information System (GIS), while Python was used to apply clustering algorithms on the data points. The resulting insights are visualized clearly through Power BI dashboards for better interpretability. This approach allows for understanding the relationship between tourists' routes and the proximity of hotels and museums. By combining AI, BI, and spatial data, more effective tourism planning can be achieved, helping to maintain well-organized and sustainable destinations.

This paper presents a methodological approach for integrating heterogeneous tourism data into a unified spatiotemporal analytical framework. By combining business intelligence, geospatial processing, and machine learning techniques, the proposed system enables a predictive and spatially aware analysis of tourist behavior. The approach is evaluated through a case study from Sarajevo Canton and demonstrates how fragmented data sources, temporal, spatial, and behavioral, can be semantically aligned to support strategic decision-making in tourism. Although the accuracy of the predictive model is constrained by data limitations, the integrated architecture reveals patterns in tourist flows and spatial clustering that are not captured by traditional methods. The main contribution lies in establishing a generalizable analytical approach to tourism intelligence, bridging data silos, and improving the analytical capabilities of destination management systems.

Citizens of the city of Sarajevo and of other industrial cities are faced with a record number of days of increased pollution. In the winter months, the city of Sarajevo faces a large number of days of pollution caused mainly by the use of fossil fuels in individual houses for heating purposes. The current situation can be changed by the massive use of energy from renewable sources such as solar energy. This paper aims to evaluate the potential of solar energy in the city of Sarajevo. The use of Geographic Information Systems (GIS) represents the most significant technological and conceptual approach to spatial data analysis. Using existing models for calculating incoming solar radiation integrated in the GRASS GIS and SAGA GIS software, we achieved the goal and calculated the results for solar energy potential in the city of Sarajevo and presented them for the specific settlements. The model was implemented on the basis of created Digital Elevation Model (DEM) from Google Earth – free datasets, using techniques to collect and convert data with different software. Comparative results of selected model research are evaluated using the collected solar irradiance values from the meteorological stations, other research results, and the solar energy potential estimated via the Photovoltaic GIS Information System (PVGIS).

The outbreak of COVID-19 is a public health emergency that caused disastrous results in many countries. The global aim is to stop transmission and prevent the spread of the disease. To achieve it, every country needs to scale up emergency response mechanisms, educate and actively communicate with the public, intensify infected case finding, contact tracing, monitoring, quarantine of contacts, and isolation of cases. Responding to an emergency requires efficient collaboration and a multi-skilled approach (medical, information, statistical, political, social, and other expertise), which makes it hard to define one interface for all. As actors from different perspectives and domain backgrounds need to address diverse functions, the possibility to exchange available information quickly would be desirable. Geoportal provides an entry point to access a variety of data (geospatial data, epidemiological data) and could be used for data discovery, view, download, and transformation. It helps to deal with challenges like data analysis, confirmed cases geocoding, recognition of disease dynamics, vulnerable groups identification, and capacity mapping. Predicting and modeling the spread of infection, along with application support for communication and collaboration, are the biggest challenges. In response to all these challenges, we have established the Epidemic Location Intelligence System (ELIS) using open-source software components in the cloud, as a working platform with all the required functionalities.

ABSTRACT The Online Biomass Potential Atlas is a tool primarily intended for geo-visualisation of biomass data from the Biomass Potential Monitoring System in Bosnia and Herzegovina. However, its role does not have to end here. By developing a functional extension, it can offer an environment for the location analysis of potential biomass users and sources of unused biomass potential. This paper describes an approach for developing tool with such functionality, based on spatial interaction modelling. Determining the optimal location for biogas plants in the region covered by the administrative units of two cantons in Bosnia and Herzegovina is considered as a case study. Based on the analysis conducted in the case study, the feasibility of applying this tool has been demonstrated.

Between March 5 and July 25, 2020, the total number of SARS-CoV-2 confirmed cases in Bosnia and Herzegovina (BH) was 10 090 corresponding to a cumulative incidence rate of 285.7 per 100 000 population. Demographic and clinical information on all the cases along with exposure and contact information was collected using a standardized case report form. In suspected SARS-CoV-2 cases, respiratory specimens were collected and tested by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) assay. The dynamic of the outbreak was summarized using epidemiological curves, instantaneous reproduction number Rt and interactive choropleth maps for geographical distribution and spread. The rate of hospitalization was 14.0% (790/5646) in Federation of Bosnia and Herzegovina (FBH) and 6.2% (267/4299) in Republic of Srpska (RS). The death rate was 2.2% (122/5646) in FBH and 3.6% in the RS (155/4299). After the authorities lifted mandatory quarantine restrictions, the basic reproduction number increased from 1.13 on May, the 20th to 1.72 on May the 31st. The outbreak concerns both entities, Federation of Bosnia and Herzegovina and Republika Srpska, and it is more pronounced in those aged 20-44 years. It is important to develop the communication and emergency plan for the SARS-CoV-2 outbreak in BH, including the mechanisms to allow the ongoing notification and updates at the national level.