Distance‐based methods are applied in various fields of research. In this paper, a new relative distance‐based method, the W function, is introduced. This method contributes to the assessment of spatial patterns of economic activities using the stochastic Monte Carlo simulation, and supplements the typology of distance‐based methods recently drawn up by Marcon and Puech. The capability of the W function is compared with results from the Kd and the recently defined m function methods, which are widely used for monitoring the spatial distribution of economic activities by considering several theoretical and empirical examples. The W function appears to provide more precise estimations of the density of economic activities compared to the m and Kd functions, particularly in cases of complex patterns such as double clustered distribution. It also appears to provide a more accurate evaluation of dispersion.

Contemporary cities require excellent walking conditions to support human physical activity, increase humans’ well-being, reduce traffic, and create a healthy urban environment. Various indicators and metrics exist to evaluate walking conditions. To evaluate the spatial pattern of objective-based indicators, two popular indices were selected—the Walkability Index (WAI), representing environmental-based indicators, and Walk Score (WS), which applies an accessibility-based approach. Both indicators were evaluated using adequate spatial units (circle buffers with radii from 400 m to 2414 m) in two Czech cities. A new software tool was developed for the calculation of WS using OSM data and freely available network services. The new variant of WS was specifically designed for the elderly. Differing gait speeds, and variable settings of targets and their weights enabled the adaptation of WS to local conditions and personal needs. WAI and WS demonstrated different spatial pattern where WAI is better used for smaller radii (up to approx. 800 m) and WS for larger radii (starting from 800 m). The assessment of WS for both cities indicates that approx. 40% of inhabitants live in unsatisfactory walking conditions. A sensitivity analysis discovered the major influences of gait speed and the β coefficient on the walkability assessment.

The optical sensors on satellites nowadays provide images covering large areas with a resolution better than 1 meter and with a frequency of more than once a week. This opens up new opportunities to utilize satellite-based information such as periodic monitoring of transport flows and parked vehicles for better transport, urban planning and decision making. Current vehicle detection methods face issues in selection of training data, utilization of augmented data, multivariate classification or complexity of the hardware. The pilot area is located in Prague in the surroundings of the Old Town Square. The WorldView3 panchromatic image with the best available spatial resolution was processed in ENVI, CATALYST Pro and ArcGIS Pro using SVM, KNN, PCA, RT and Faster R-CNN methods. Vehicle detection was relatively successful, above all in open public places with neither shade nor vegetation. The best overall performance was provided by SVM in ENVI, for which the achieved F1 score was 74%. The PCA method provided the worst results with an F1 score of 33%. The other methods achieved F1 scores ranging from 61 to 68%. Although vehicle detection using artificial intelligence on panchromatic images is more challenging than on multispectral images, it shows promising results. The following findings contribute to better design of object-based detection of vehicles in an urban environment and applications of data augmentation.

The subject of this research is one of the main preconditions for the provision of high-quality social care services for people over the age of 65 with lowered self-sufficiency. It involves the spatial accessibility of formally established nursing services examined in 76 districts of the Czech Republic. The aim of this article is to identify and evaluate the gaps in spatial accessibility of the selected residential and outpatient-clinic services at the level of districts in individual regions of the Czech Republic in 2018. A three-phase analysis was performed, including an ArcGIS network analysis, multi-criteria evaluation according to the TOPSIS method, and a correlation analysis encompassing the confidence interval gained via the Bootstrap method. Seven indicators were selected—recipients of the allowance for the care, capacity of residential and outpatient-clinic services, and four indicators of accessibility via individual and public transport within the set time intervals. The results show good availability of residential care (no gap) within 30 min. by individual and public transport in most districts (94%). However, day services centers do not have a space gap in only 28% of districts by individual transport, and 8% of districts by public transport. In the case of day care centers, 54% of districts by individual transport, and 29% of districts by public transport do not have a space gap. The results also show that the level of spatial availability of care (gaps) in the district is not related to the number of people aged 65+ with reduced self-sufficiency in the district. On the contrary, the correlation analysis shows that with the growing number of people aged 65+ with reduced self-sufficiency in the district, the capacity of residential and outpatient services increases and the gaps in spatial accessibility do not decrease.

Key factor in the crisis management decision-making process is complete, easy-to-use, and quickly available spatial information on protected interests, preparedness, and vulnerability in the area. Technology progression of geographic information systems help us significantly simplify and speed up this process. It helps us to visualize this essential information for the strategic level of crisis management.