Iwona Kaczmarek
Biographic Data
| ID | 4624966 |
|---|---|
| NAME | Iwona Kaczmarek |
| GIVEN NAMES | Iwona |
| FAMILY NAME | Kaczmarek |
| SIGNATURE | KACZMAREK I |
| AFFILIATIONS | Wrocław University of Environmental and Life Sciences |
| ORCID | 0000-0002-0738-484X |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Understanding city dynamics by analyzing investment processes - an integrated formal and semantic approach
Understanding investment-construction processes and their dynamics is crucial for effective urban planning, but these processes are often complex and fragmented. While advances in GeoAI offer powerful new tools for analysis, conventional approaches to studying urban development face two key limitations. First, they frequently neglect to represent the processual characteristics of development, regarding complex, multi-phase projects as a successio…
Integrating media sentiment with traditional economic indicators
Global crises, such as wars or the COVID-19 pandemic, underscore the need for real-time economic monitoring. Traditional economic indicators often fall short, prompting the exploration of alternative data sources, including online and social media content. This study examines the relationship between media sentiment in press articles and traditional economic indicators: the Purchasing Managers' Index (PMI), Consumer Confidence Index (CCI), and av…
Classification of Spatial Objects with the Use of Graph Neural Networks
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topolog…
A machine learning approach for integration of spatial development plans based on natural language processing
Spatial development plans are the basic tool for shaping spatial policy and have an impact on the implementation of the concept of sustainable development. Monitoring the implementation of plans can be difficult where no standard of plans exists that allows for obtaining comprehensive information on the arrangements of the plans, including future land development. The purpose of the research is to integrate spatial development plans by analyzing …
Spatial Analysis of Asymmetry in the Development of Tourism Infrastructure in the Borderlands
This paper discusses the issue of analyzing the development of cross-border tourism infrastructure in the borderlands of countries with diversified administrative divisions and spatial databases, which hinders the use of national statistical units for comparative research. As an example, the ability to use the square grid and kernel density estimation methods for the analysis and spatial visualization of the level of tourism infrastructure develo…
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Spatial Analysis of Asymmetry in the Development of Tourism Infrastructure in the Borderlands
This paper discusses the issue of analyzing the development of cross-border tourism infrastructure in the borderlands of countries with diversified administrative divisions and spatial databases, which hinders the use of national statistical units for comparative research. As an example, the ability to use the square grid and kernel density estimation methods for the analysis and spatial visualization of the level of tourism infrastructure develo…
A machine learning approach for integration of spatial development plans based on natural language processing
Spatial development plans are the basic tool for shaping spatial policy and have an impact on the implementation of the concept of sustainable development. Monitoring the implementation of plans can be difficult where no standard of plans exists that allows for obtaining comprehensive information on the arrangements of the plans, including future land development. The purpose of the research is to integrate spatial development plans by analyzing …
Classification of Spatial Objects with the Use of Graph Neural Networks
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topolog…
Integrating media sentiment with traditional economic indicators
Global crises, such as wars or the COVID-19 pandemic, underscore the need for real-time economic monitoring. Traditional economic indicators often fall short, prompting the exploration of alternative data sources, including online and social media content. This study examines the relationship between media sentiment in press articles and traditional economic indicators: the Purchasing Managers' Index (PMI), Consumer Confidence Index (CCI), and av…
Understanding city dynamics by analyzing investment processes - an integrated formal and semantic approach
Understanding investment-construction processes and their dynamics is crucial for effective urban planning, but these processes are often complex and fragmented. While advances in GeoAI offer powerful new tools for analysis, conventional approaches to studying urban development face two key limitations. First, they frequently neglect to represent the processual characteristics of development, regarding complex, multi-phase projects as a successio…
Computer Science (4 works) · Geography (4 works) · Artificial Intelligence (3 works) · Geographic Information Systems Studies (3 works) · Data mining (2 works) · Graph (2 works) · Machine learning (2 works) · Mathematics (2 works) · Spatial analysis (2 works) · 2019-20 coronavirus outbreak (1 work)