YMS team prepared a demonstrative geoanalysis of COVID-19 impacts on municipalities

The analysis of data, which characterise municipalities from the point of view of economic vulnerability, allows for systematic targeting of measures to mitigate negative trends after an emergency. Above all it will be the migration of populations into cities, the liquidation of small farms, possibly local companies. Among the parameters, that need to be taken into account, is not only unemployment before an emergency, but above all data about the income of a population, the number of employers, types of businesses. An important indicator is also the diversity of incomes in a single household, unfortunately this data isn’t accessible. We recommend that the future census also focuses on obtaining data useful in determining the socio economic stability of a system, including the level of flexibility (resilience) – to what extent they will have the strength to return to their pre-emergency situation.

It is based on the analysis of all transport networks throughout Slovakia. All the roads were classified based on the calculated traffic density within the project Open Transport Network. Comparing the results with the road class revealed however, that the data about theoretical traffic density aren’t relevant. More accurate approaches could be updated using data from mobile operators or from navigation system owners (eg Google). We calculated the Euclidean distance from roads and classified it into categories based on accessibility (easy accessibility by foot, medium accessibility, accessibility via individual non-motorised means of transport and accessibility via motorised vehicles). Similarly we added the distance to railway stations into the remoteness analysis. The remoteness index can be interpreted as a basis for calculating the expected rate of spread of disease into the statistical models currently in use.

This analysis is notably important especially in municipalities, where the high risk age groups are more active and at the same time less informed. In practice it means that the residents ignore necessary face protections, ignore the ban on visiting churches, cemeteries, go shopping, and meet up. Included were also parks and petrol stations. Data about businesses with numbers of employees are only available at the district level, and were therefore not included. In the case of calculations at regional levels they can be incorporated. All the listed factors were taken into account and we calculated the Euclidean distance from these objects. The population gathering index can be interpreted as places, where the spread of viruses can occur easier between residents. Municipalities can monitor and guide their residents in regard to these places.

The final factor is building density, which represents to what extent the inhabitants of houses/living spaces are in vicinity, or in isolation. Especially in residential structures with a lower building density it is the rate at which diseases spread are not predicted to be high. Density was calculated based on the ratio of the built-up area to the total area on elementary units of 250 × 250 meters.

Index of the threat of spreading the virus COVID-19 among the population was finally calculated using a combination of the index of remoteness and the index of gathering places of the population. The scales between them were set in the ratio of 60:40 for the index of building density to/vs the other indices of remoteness and gatherings/assemblies. The index can also be interpreted as possible centers of further incidences of COVID-19 between the population. In the designated areas it is necessary to monitor and test the population.

The final analysis was performed as a synthesis of the vulnerability index and spread threat index gradients. The result is a qualitative evaluation of individual municipalities. Despite the fact that the threat indices were calculated on a network of 250 x 250 meters, data about age composition was only available at the municipal level. On the other hand it allows for more focused targeting of safety measures to local governments with attention to possible risks and possibly even the tightening of generally applicable regulations.

The analysis does not aim to directly invoke safety measures. It was created as an example of the possibilities of geospatial analysis that can be made with data with the goal to support decision-making in emergency situations, and to do so for crisis staffs of different hierarchical levels. If more detailed data was available it is possible to refine the analysis, but also to expand it and analyze the necessary safety precautions of medical staff and materials.

By expanding with the current incidents of infection, with databases about tested persons with negative results, or maybe with databases about persons in quarantine, they will allow the calculations of even more targeted outputs, that can help operational management and ultimately reduce the negative impact of the present situations.

All the data, outputs and algorithms and configured in an analytical system, which allows you to modify the calculation model based on the needs and requirements, dynamically refine the outputs by adding more detailed data, and create outputs for different scales, or aggregate them into hierarchically higher units.