<abstract xmlns="http://www.w3.org/1999/xhtml">

Multi-hazard mapping in urban areas is relevant for preventing and mitigating the impact of nature- and human-induced disasters while being a challenging task as different competencies have to be put together. Artificial intelligence models are being increasingly exploited for single-hazard susceptibility mapping, from which multi-hazard maps are ultimately derived. Despite the remarkable performance of these models, their application requires the identification of a list of conditioning factors as well as the collection of relevant data and historical inventories, which may be non-trivial tasks. The objective of this study is twofold. First, based on a review of recent publications, it identifies conditioning factors to be used as an input to machine and deep learning techniques for singlehazard susceptibility mapping. Second, it investigates open datasets describing those factors for two European cities, namely Milan (Italy) and Sofia (Bulgaria) by exploiting local authorities’ databases. Identification of the conditioning factors was carried out through the review of recent publications aiming at hazard mapping with artificial intelligence models. Two indicators were conceived to define the relevance of each factor. A first research result consists of a relevance-sorted list of conditioning factors per hazard as well as a set of open and free access data describing several factors for Milan and Sofia. Based on data availability, a feasibility analysis was carried out to investigate the possibility to model hazard susceptibility for the two case studies as well as for the limit case of a city with no local data available. Results show major differences between Milan and Sofia while pointing out Copernicus services’ datasets as a valuable resource for susceptibility mapping in case of limited local data availability. Achieved outcomes have to be intended as preliminary results, as further details shall be disclosed after the discussion with domain experts.
</abstract>

Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia

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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.
</abstract>

Vehicle detection using panchromatic high-resolution satellite images as a support for urban planning. Case study of Prague’s centre

<abstract xmlns="http://www.w3.org/1999/xhtml">

Cities serve as hubs for various activities that necessitate comprehensive transportation connectivity. This study examines the decadal urban agglomeration patterns from 2001 to 2020 and critically assesses the relationship between freeway developments, industrial relocation, and population density in the DFW (Dallas Fort Worth) metropolitan area. Landsat satellite imageries, US census, and open-source GIS datasets have been utilized in the study. The Maximum Likelihood Classification (MLC) algorithm helped generate the vector database, using which Land Use/Land Cover (LULC) variations were assessed. The calculated overall accuracies of the classified images for 2001, 2011, and 2020 were 93.12%, 91.87%, and 93.12%, respectively. Eventually, buffer generation techniques and summary statistics helped detect potential boom hotspots. Our results indicate that the highway advancement project lures industries, leading to population migration. The LULC variations suggest that the increase in highway infrastructure resulted in a surge in built-up and a decrease in open spaces in District-3 of DFW. From our study, we find that 79.16% of old industries are located near old freeways, while 78.84% of new industries are located near new freeways. Further, our industrial area to road area comparison clearly shows that industrial relocation was driven by transportation advancements over time. Our results also confirm that this relocation of industries fostered a massive population influx during the following decades.
</abstract>

Department of Public Affairs and Planning

Effects of Transport Corridor Advancement on Agglomeration and Industrial Relocation – Dallas Fort Worth (US) case study

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Historically, industry shaped the space-economy of the American city, a major source of employment opportunity for residents that selected housing nearby or within a convenient or affordable commuting distance. However, the contemporary American city is structurally characterized by abandoned, blighted, vacant industrial properties due to urban expansion, deindustrialization and the suburbanization of both jobs and population. The urban studies literature rarely documents the neighborhood impact of industrial blight akin to studies of residential blight. We determine the proximity-effect of industrial blight on the neighborhood thought of not as an isolated and closed entity, but as a connected and open entity within the city and the region. Unlike studies confined to the property value impact, we determine Pearson correlations of industrial blight and vacancy expansively with the socio-economic and physical characteristics of neighborhoods. We use path analysis to determine direct, indirect, and total neighborhood impact of industrial blight and vacancy. The census block group and parcel-level geographic information system (GIS) provide our principal sources of data. The block group geography contains the neighborhood as a fundamental spatial unit. We determine how the neighborhood impact varies with distance from the blighted, vacant industrial property.
</abstract>

The Neighborhood Impact of Industrial Blight: A Path Analysis

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In this article, we analyze academic patenting on an unprecedented scale - for 29 European countries. The comprehensive identification phase captures both groups of academic patents, in and outside of the ownership of universities. With stronger patent rights of universities, the share of university-owned patents is increasing faster. Nevertheless, even today, universities own only one third of their patents while the other two thirds remain in the property of individuals and companies. Universities have recently accounted for 13% of regional patent production and compared to firms their contribution to region’s innovation performance remains small. The share of academic patents is higher in Eastern Europe and in regions where innovative companies are lacking, in capital regions, and in regions with a strong tradition in academic patenting. The contribution of universities to the innovation performance of the most innovative regions is relatively small, although there are large differences, and the size of the contribution can vary significantly for similar levels of patent intensity. Given the effects of university research, it has been known since the 1990s that increasing university spending on research is driving innovation on the part of companies and within the region. However, such an effect is limited to high quality research and is most effective in terms of regional policy in poor regions where innovative companies are lacking. In this paper, instead of research expenditure, university patents filed with the EPO represent university research, as other patents are assumed to be a product of companies. We have found that the latter can be predicted by the volume of academic patents.
</abstract>

Development of Academic Patenting in European Regions – A Large Scale Analysis

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Policies in liberal democracies can never be fully sheltered from the influence of interest groups. This is especially true for the flawed or immature democracies that can be found also in the post-communist world. In this study, we argue that the progress of democratization in Czechia has been accompanied by a growing influence of interest groups (local/regional governments, political parties, industrial corporations) on national defence strategy. Focusing on the spatial policy of military base distribution, the study documents deviations from a rational strategy of restructuring and relocations of military bases declared by the General Staff in the 1990s, 2000s, and 2010s in response to a changing geopolitical situation. The content analysis of the interviews with former actors of the defence planning and media analysis showed that one-third of current Czech military bases experienced an influence of interested groups.
</abstract>

Department of Human Geography and Regional Development, Faculty of Science

Spatial distribution of military bases and power of interest groups in an emergent democracy

AHEAD OF PRINT

Volume 17 (2023): Issue 2 (December 2023)

Volume 17 (2023): Issue 1 (June 2023)

Volume 16 (2022): Issue 2 (December 2022)

Volume 16 (2022): Issue 1 (June 2022)

Volume 15 (2021): Issue 2 (December 2021)

Volume 15 (2021): Issue 1 (June 2021)

Volume 14 (2020): Issue 2 (December 2020)

Volume 14 (2020): Issue 1 (June 2020)

Volume 13 (2019): Issue 2 (December 2019)

Volume 13 (2019): Issue 1 (June 2019)

Volume 12 (2018): Issue 2 (December 2018)

Volume 12 (2018): Issue 1 (June 2018)

Volume 11 (2017): Issue 2 (December 2017)

Volume 11 (2017): Issue 1 (June 2017)

Volume 10 (2016): Issue 2 (December 2016)

Volume 10 (2016): Issue 1 (June 2016)

Volume 9 (2015): Issue 2 (December 2015)

Volume 9 (2015): Issue 1 (June 2015)

Volume 8 (2014): Issue 2 (December 2014)

Volume 8 (2014): Issue 1 (June 2014)

GeoScape

The journal was established in 2006 strictly following the Open-Access policy. Its editorial board makes its best to protect the journal’s status as a non-profit publication platform free of any APC (Article Processing Charge). It is also for this reason that the journal is published only in electronic form. Volumes preceding 2014 can be found at lsru.geography.ujep.cz/research.html  The journal focuses on landscape and territorial research in its broadest meaning. It addresses the territorial organization of society and environment from the perspective of various hierarchic levels, functional and developmental dimensions, and their mutual relations. The aim of the journal is to establish a common platform for the publication of research on ecological and environmental issues, social dimension of sustainability, economic geography and regional development, cross-border cooperation and political strategies influencing the landscape, spatial planning, regional development, and policies at different scales. The interdisciplinarity is strongly encouraged.  In all its content the journal accentuates the practical implications of the research for public administration, planning, and regional development. Therefore, ideally, the papers discuss the relevance of the methodical procedures and of the results for the respective fields.  Submission and peer-review process  The journal welcomes papers that suite one of the following categories: original papers, research reports and discussion papers (see author guidelines for details). The journal also publishes Special Issues or papers grouped around particular themes (Thematic papers). The publication of Special and Thematic Issues is subject to approval of the editorial board based on evaluation procedure of the submitted proposals. Papers prepared according to the Instructions for Authors should be submitted directly to the Managing Editor (pavel.raska[at]ujep.cz).  All manuscripts are subject to initial evaluation by editorial board (Editor-in-chief, Managing Editor, and potential handling Editor). After the positive initial evaluation, the manuscript proceeds to double-blind peer-review.  Why subscribe and read　  The journal addresses researchers in geographical and environmental fields, professionals as well as decision makers in social sciences and regional development, planning, public administration, and policy. It provides up-to-date research results together with discussion of its implications for the respective fields.　  Why submit    International audience  Open-access policy  Rapid peer-review process  Practical implications of research results   Rejection Rate　    30−40 %   Copyright and Open-Access Policy  All papers are published as gold Open-Access (the non-commercial use of the article governed by the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 license) without incurring any APC to the authors. The Open-Access Policy is detailed in an Open-Access Licence in the For Authors section.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 