Geoinformatics and Remote Sensing (GId)
Multiscale assessment of divergent and convergent trends in the spatial social-economic stratification of society
The aim of the dissertation is to examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools. In the thesis, it will be necessary to identify appropriate indicators for different spatial scales and evaluate the appropriateness, availability and reliability of particular data. The data will be applied to the required spatial units using areal transformation methods and similar data used at the international level for comparison of urban areas such as the data from European Land Monitoring Services (Urban Atlas). Subsequently, the analysis and modelling of the spatial arrangement of social-economic stratification will be carried out with an emphasis on the chronological aspect and identification of factors leading to this stratification. Advanced statistical methods and tools of geographic information systems will be used. The thesis will also identify regularities of the spatial arrangement at various scales and with an emphasis on their evaluation in causal contexts.
To examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools.
doc. Mgr. Ladislav Novotný, PhD.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GIdAj)
Multiscale assessment of divergent and convergent trends in the spatial social-economic stratification of society
The aim of the dissertation is to examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools. In the thesis, it will be necessary to identify appropriate indicators for different spatial scales and evaluate the appropriateness, availability and reliability of particular data. The data will be applied to the required spatial units using areal transformation methods and similar data used at the international level for comparison of urban areas such as the data from European Land Monitoring Services (Urban Atlas). Subsequently, the analysis and modelling of the spatial arrangement of social-economic stratification will be carried out with an emphasis on the chronological aspect and identification of factors leading to this stratification. Advanced statistical methods and tools of geographic information systems will be used. The thesis will also identify regularities of the spatial arrangement at various scales and with an emphasis on their evaluation in causal contexts.
To examine the development trends in the spatial social-economic stratification of society at various spatial levels – from global to intra-urban using various geospatial data and tools.
Brzezinski, M. 2018: Income inequality and the Great Recession in Central and Eastern Europe. Economic Systems, 42(2), 219-247. Majzlíková, E., Vitáloš, M. 2021: Department of Economic Policy Working Paper Series, No. 24: Potential risk of automation for employment in Slovakia: A district- and industry-level analysis. Bratislava (University of Economics in Bratislava). Van Ham, M., Tammaru, T., Ubarevičiené, R., Janssen, H. eds. 2021: Urban Socio-Economic Segregation and Income Inequality - A Global Perspective. Cham (Springer). Xu, W., Engelman, M., Fletcher, J. 2021: From convergence to divergence: Lifespan variation in US states, 1959–2017. SSM: Population Health, 16, 100987.
doc. Mgr. Ladislav Novotný, PhD.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GId)
The Urban Overheating: Modeling, Consequences and Mitigation
The aim of this study is to model the urban overheating for selected meteorological situations and identify locations in the city of Košice that are most affected by overheating using appropriate meso- and micro-scale meteorological models (e.g., WRF, ENVI-met, etc.), models of solar radiation distribution and surface temperature in GRASS GIS, as well as data from the Earth Observation techniques. In the identified locations, evaluate the size and structure of the population affected by overheating, and thus determine the most threatened locations and populations in the city. For these problematic locations and meteorological situations, we will propose possible mitigation measures to reduce urban surface and ambient air temperature taking into account existing regulatory restrictions in the city. The measures will be proposed up to the level of individual buildings or streets. Measures in the form of modification of the input geospatial data will be used to recalculate the models to quantify their effectiveness. The proposed measures will be communicated with interested parties, including residents, in order to determine the perception and support of these measures. The survey will be conducted using online perception maps, and a structured questionnaire survey. The results will contribute to the development of methodology for addressing the problem of urban overheating and to define an optimal implementation strategy, including a communication strategy for all interested parties.
AGHAMOHAMMADI, N., SANTAMOURIS, M. (2023). Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, https://doi.org/10.1007/978-981-19-4707-0_18. AKMAR, A. N., KONIJNENDIJK, C., SREETHERAN, M., NILSSN, K. J. A. (2011). Greenspace planning and management in Klang valley, Peninsular Malaysia. Urban Forestry & Urban Greening, 37(3), pp. 99-107. ALI, S. B., PATNAIK, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, pp. 954-967. BECKMANN, S. K., HIETE, M. (2020). Predictors Associated with Health-Related Heat Risk Perception of Urban Citizens in Germany. International Journal of Environmental Research and Public Health, 17(3), 874. CHEN, B., XIE, M., FENG, Q., LI, Z., CHU, L., LIU, Q. (2021). Heat risk of residents in different types of communities from urban heat-exposed areas. Science of The Total Environment, 768, 145052. DERKZEN, M. L., VAN TEEFFELEN, A. J. A., VERBURG, P. H. (2017). Green infrastructure for urban climate adaptation: how do residents’ views on climate impacts and green infrastructure shape adaptation preferences? Landscape and Urban Planning, 157, pp. 106-130. FRANCIS, R. A., LORIMER, J. (2011). Urban reconciliation ecology: The potential of living roofs and walls. Journal of Environmental Management, 92(6), pp. 1429-1437. HAALAND, C., VAN DEN BOSCH, C. K. (2015). Challenges and strategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry & Urban Greening, 14(4), pp. 760-771. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. HOLEC, J., ŠVEDA, M., SZATMÁRI, D., FERANEC, J., BOBÁĽOVÁ, H., KOPECKÁ, M., ŠŤASTNÝ, P. (2021). Heat risk assessment based on mobile phone data: case study of Bratislava, Slovakia. Natural Hazards, 108(3), pp. 3099-3120. IHA (2022). Institute of Health Analysis at the Ministry of Health of the Slovak Republic. Available at: https://www.health.gov.sk/?iza. KHATIBI, F. S., DEDEKORKUT-HOWES, A., HOWES, M., TORABI, E. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability, 2(1), pp. 1-24. KULLA, M., NOVOTNÝ, L., PREGI, L., DVOŘÁK, P., MARTINÁT, S., KLUSÁČEK, P., NAVRÁTIL, J., FRANTÁL, B. (2022). The Good, the Bad, and the Nobody. Exploring diversity of perceptions of anaerobic digestion plants in Central and Eastern Europe. Energy Research and Social Sciences, 89, 102644. KOPECKÁ M., SZATMÁRI, D., HOLEC, J., FERANEC, J. (2021). Urban heat island modelling based on MUKLIMO: examples from Slovakia. AGILE: GIScience Series Series, 2(5), pp. 1-11. LEHMANN, S. (2014). Low carbon districts: Mitigating the urban heat island with green roof infrastructure. City, Culture and Society, 5(1), pp. 1-8. LINDBERG, F. (2007). Modelling the urban climate using a local governmental geo-database. Meteorol. Appl. 14, pp. 263–273. LOUGHNAN, M., NICHOLLS, N., TAPPER, N. J. (2012). Mapping heat health risks in urban areas. International Journal of Population Research, 518687. MIRZAEI, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. MOSER, S. C., PIKE, C. (2015). Community engagement on adaptation: Meeting a growing capacity need. Urban Climate, 14, pp. 111–115. ONAČILLOVÁ, K., GALLAY, M. (2018). Spatio-temporal analysis of surface urban heat island based on LANDSAT ETM+ and OLI/TIRS imagery in the city of Košice, Slovakia. Carpathian Journal of Earth and Environmental Sciences, 13(2), pp. 395–408. PARSAEE, M., JOYBARI, M. M., MIRZAEI, P. A., HAGHIGHAT, F. (2019). Urban heat island, urban climate maps and urban development policies and action plans. Environmental Technology & Innovation, 14, 100341. RAJAGOPALAN, P., SANTAMOURIS, M., ANDAMON, M. M. (2017). Public engagement in urban microclimate research: an overview of a citizen science project. In Schnabel, M. A. (ed). Back to the future: the next 50 years. Wellington (Architectural Science Association), pp. 703-712. SANDHOLZ, S., SETT, D., GRECO, A., WANNEWITZ, M., GARSCHAGEN, M. (2021). Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Climate, 37, 100857. SARHADI, F., RAD, V. B. (2020). The structural model for thermal comfort based on perceptions individuals in open urban spaces. Building and Environment, 185, 107260. TAN, P. Y., WANG, J., SIA, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, pp. 24-32. TIAN, Y., JIM, C. Y. (2012). Development potential of sky gardens in the compact city of Hong Kong. Urban Forestry & Urban Greening, 11(3), pp. 223-233. WANG, CH., WANG, Z. H., KALOUSH, K. E., SHACAT, J. (2021). Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis. Sustainable Cities and Society, 66, 102687. WANG, Y, BERARDI, U., AKBARI, H. (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings 114, pp. 2-19.
prof. Mgr. Jaroslav Hofierka, PhD.
Geoinformatics and Remote Sensing (GIdAj)
The Urban Overheating: Modeling, Consequences and Mitigation
The aim of this study is to model the urban overheating for selected meteorological situations and identify locations in the city of Košice that are most affected by overheating using appropriate meso- and micro-scale meteorological models (e.g., WRF, ENVI-met, etc.), models of solar radiation distribution and surface temperature in GRASS GIS, as well as data from the Earth Observation techniques. In the identified locations, evaluate the size and structure of the population affected by overheating, and thus determine the most threatened locations and populations in the city. For these problematic locations and meteorological situations, we will propose possible mitigation measures to reduce urban surface and ambient air temperature taking into account existing regulatory restrictions in the city. The measures will be proposed up to the level of individual buildings or streets. Measures in the form of modification of the input geospatial data will be used to recalculate the models to quantify their effectiveness. The proposed measures will be communicated with interested parties, including residents, in order to determine the perception and support of these measures. The survey will be conducted using online perception maps, and a structured questionnaire survey. The results will contribute to the development of methodology for addressing the problem of urban overheating and to define an optimal implementation strategy, including a communication strategy for all interested parties.
AGHAMOHAMMADI, N., SANTAMOURIS, M. (2023). Urban Overheating: Heat Mitigation and the Impact on Health. Advances in Sustainability Science and Technology. Springer, https://doi.org/10.1007/978-981-19-4707-0_18. AKMAR, A. N., KONIJNENDIJK, C., SREETHERAN, M., NILSSN, K. J. A. (2011). Greenspace planning and management in Klang valley, Peninsular Malaysia. Urban Forestry & Urban Greening, 37(3), pp. 99-107. ALI, S. B., PATNAIK, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, pp. 954-967. BECKMANN, S. K., HIETE, M. (2020). Predictors Associated with Health-Related Heat Risk Perception of Urban Citizens in Germany. International Journal of Environmental Research and Public Health, 17(3), 874. CHEN, B., XIE, M., FENG, Q., LI, Z., CHU, L., LIU, Q. (2021). Heat risk of residents in different types of communities from urban heat-exposed areas. Science of The Total Environment, 768, 145052. DERKZEN, M. L., VAN TEEFFELEN, A. J. A., VERBURG, P. H. (2017). Green infrastructure for urban climate adaptation: how do residents’ views on climate impacts and green infrastructure shape adaptation preferences? Landscape and Urban Planning, 157, pp. 106-130. FRANCIS, R. A., LORIMER, J. (2011). Urban reconciliation ecology: The potential of living roofs and walls. Journal of Environmental Management, 92(6), pp. 1429-1437. HAALAND, C., VAN DEN BOSCH, C. K. (2015). Challenges and strategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry & Urban Greening, 14(4), pp. 760-771. HOFIERKA, J. (2022). Assessing land surface temperature in urban areas using open-source geospatial tools. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 48(4/W1-2022), 195-200. HOFIERKA, J., GALLAY, M., ONAČILLOVÁ, K., HOFIERKA, J. Jr. (2020). Physically-based land surface temperature modeling in urban areas using a 3-D city model and multispectral satellite data. Urban Climate, 31, 100566. HOLEC, J., ŠVEDA, M., SZATMÁRI, D., FERANEC, J., BOBÁĽOVÁ, H., KOPECKÁ, M., ŠŤASTNÝ, P. (2021). Heat risk assessment based on mobile phone data: case study of Bratislava, Slovakia. Natural Hazards, 108(3), pp. 3099-3120. IHA (2022). Institute of Health Analysis at the Ministry of Health of the Slovak Republic. Available at: https://www.health.gov.sk/?iza. KHATIBI, F. S., DEDEKORKUT-HOWES, A., HOWES, M., TORABI, E. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies? Discover Sustainability, 2(1), pp. 1-24. KULLA, M., NOVOTNÝ, L., PREGI, L., DVOŘÁK, P., MARTINÁT, S., KLUSÁČEK, P., NAVRÁTIL, J., FRANTÁL, B. (2022). The Good, the Bad, and the Nobody. Exploring diversity of perceptions of anaerobic digestion plants in Central and Eastern Europe. Energy Research and Social Sciences, 89, 102644. KOPECKÁ M., SZATMÁRI, D., HOLEC, J., FERANEC, J. (2021). Urban heat island modelling based on MUKLIMO: examples from Slovakia. AGILE: GIScience Series Series, 2(5), pp. 1-11. LEHMANN, S. (2014). Low carbon districts: Mitigating the urban heat island with green roof infrastructure. City, Culture and Society, 5(1), pp. 1-8. LINDBERG, F. (2007). Modelling the urban climate using a local governmental geo-database. Meteorol. Appl. 14, pp. 263–273. LOUGHNAN, M., NICHOLLS, N., TAPPER, N. J. (2012). Mapping heat health risks in urban areas. International Journal of Population Research, 518687. MIRZAEI, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. MOSER, S. C., PIKE, C. (2015). Community engagement on adaptation: Meeting a growing capacity need. Urban Climate, 14, pp. 111–115. ONAČILLOVÁ, K., GALLAY, M. (2018). Spatio-temporal analysis of surface urban heat island based on LANDSAT ETM+ and OLI/TIRS imagery in the city of Košice, Slovakia. Carpathian Journal of Earth and Environmental Sciences, 13(2), pp. 395–408. PARSAEE, M., JOYBARI, M. M., MIRZAEI, P. A., HAGHIGHAT, F. (2019). Urban heat island, urban climate maps and urban development policies and action plans. Environmental Technology & Innovation, 14, 100341. RAJAGOPALAN, P., SANTAMOURIS, M., ANDAMON, M. M. (2017). Public engagement in urban microclimate research: an overview of a citizen science project. In Schnabel, M. A. (ed). Back to the future: the next 50 years. Wellington (Architectural Science Association), pp. 703-712. SANDHOLZ, S., SETT, D., GRECO, A., WANNEWITZ, M., GARSCHAGEN, M. (2021). Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Climate, 37, 100857. SARHADI, F., RAD, V. B. (2020). The structural model for thermal comfort based on perceptions individuals in open urban spaces. Building and Environment, 185, 107260. TAN, P. Y., WANG, J., SIA, A. (2013). Perspectives on five decades of the urban greening of Singapore. Cities, 32, pp. 24-32. TIAN, Y., JIM, C. Y. (2012). Development potential of sky gardens in the compact city of Hong Kong. Urban Forestry & Urban Greening, 11(3), pp. 223-233. WANG, CH., WANG, Z. H., KALOUSH, K. E., SHACAT, J. (2021). Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis. Sustainable Cities and Society, 66, 102687. WANG, Y, BERARDI, U., AKBARI, H. (2016). Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy and Buildings 114, pp. 2-19.
prof. Mgr. Jaroslav Hofierka, PhD.