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.

Abstract:With the rise in the number of applications using geospatial data and the number of GIS applications, the number of people who come into contact with geospatial data is increasing, too. Despite many attempts to introduce standardized formats in this area, they are often ignored by software developers as well as the users themselves for various reasons. When creating or exporting geographical data, users choose the format with regard to the software they use, or for which the data are intended. Users then have to deal with conversion of data formats, and considering its use also the issue of their transformation to the appropriate spatial reference system. This work presents findings related to this issue, obtained from several years of operation of an online service for the conversion and transformation of geographical data which is heavily used by users from all over the world. It presents statistics of individual formats use and spatial reference systems of geospatial data use from the point of view of both input and output data. The results, besides other things, are shown in the form of a pie chart map in which various needs of users from a variety of countries can be seen. The results of this work can be used especially by developers of applications which use geospatial data; it will allow them to quickly understand current user needs.

Currently, ongoing global climate change brings, among other things, an increasing frequency of extreme weather events such as heavy rains, which can cause flash floods. Responsible authorities have tried to develop systems for early warning of such events. Such systems already exist in the US and in some European countries. They often rely on the prediction of extreme rainfall, possibly with the use of weather radar data, as well as rainfall-runoff models. The weakness in these systems, which limits their global usage, is based on the precise use of rainfall-runoff models and the attempt to quantify the impacts of extreme rainfall in the affected area. Therefore, we have developed a methodology based on the simplified data inputs (data from weather radar) that release a warning for potentially vulnerable areas in the longest time possible before extreme rainfall effects are due to occur. Our ambition is not to quantify these effects. Due to the short time interval between downpours and flash floods caused by them, we do not consider this information to be significant. We decided to test our methodology inter alia on a case of regional flooding, which was the result of regional precipitations combined with extreme local rains. The results presented in this paper show that, even in this situation, the proposed methodology allows us to provide an early warning for the population to take refuge in a safe area.

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.

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