Klimaat
In this course, participants will learn how to use crowdsourced meteorological data from personal weather stations to analyze local climate patterns. By combining weather station data, IoT sensors, and GIS techniques, a detailed picture of microclimates within cities emerges. Students gain insight into how this data can be collected, analyzed, and visualized to better understand how urban environments respond to climate change and what role this information can play in climate adaptation.
In urban areas, the effects of climate change are becoming increasingly apparent. Heat waves, localized flooding, and significant temperature variations within a single city require accurate and high-resolution measurement data. Traditional meteorological networks are valuable for this purpose, but often lack the detail needed to accurately capture local variations. This is precisely why personal weather stations and IoT sensors are playing an increasingly important role in modern climate analysis.
Using crowdsourced meteorological data, temperature, humidity, wind, and precipitation can be measured on a much smaller scale. This makes it possible to visualize microclimates in cities and better understand how buildings, green spaces, paved surfaces, and water influence the local climate. Combining this data with GIS creates a powerful tool for analysis, visualization, and decision-making regarding climate adaptation.
In this course at Geo-ICT, you will learn how data from personal weather stations and sensor networks can be collected, verified, and processed into usable spatial information. You will gain insight into the quality of sensor data, learn to recognize anomalies, and discover how to use GIS to analyze patterns relevant to urban climate research and climate adaptation.
Knowledge of GIS is not required, but is recommended. Those with little experience with geographic information systems can take our basic QGIS course beforehand to familiarize themselves with the most important features.
The use of personal weather stations is growing rapidly worldwide. Individuals, organizations, and municipalities are increasingly collecting local weather data via compact sensors and online platforms. This development offers new opportunities for urban climate research, as measurements at the street or neighborhood level become available. This allows local differences to be studied much more effectively than with official weather stations alone.
This data is valuable for, among other things:
In this course, you will learn not only why this data is relevant, but also how to use the information responsibly in analyses and maps.
GIS plays a central role in processing and interpreting crowdsourced meteorological data. Individual measurement points from weather stations only become truly meaningful when they are analyzed spatially and combined with other geographic data. Think of land use, building density, green structures, elevation data, and paved surfaces.
With GIS, you can:
The course demonstrates how GIS helps transform raw sensor data into usable climate information for research, policy, and practical applications.
An important part of the course is learning to work with data from personal weather stations and IoT sensors. You’ll learn about the origins of this data and how it can be made available for further analysis via platforms, exports, or API connections.
We pay close attention to data quality. Personal weather stations are not always located in meteorologically ideal spots. Sensors may be mounted on roofs, balconies, or in direct sunlight, which can skew measurements. That’s why you’ll learn:
Once the data is prepared, you’ll get to work on spatial analysis. Here, you’ll learn how point data from weather stations can be converted into maps that visualize urban temperature and climate effects. The course covers, among other things:
This will teach you how to use GIS to gain valuable insights from sensor data that would otherwise remain fragmented and difficult to interpret.
The course focuses explicitly on the practical application of the analyses. The information from personal weather stations and GIS can be directly applied to issues surrounding climate adaptation and urban development. You will learn how this data can contribute to:
In this way, the course bridges the gap between technology, geodata, and concrete challenges in urban practice.
At Geo-ICT, we combine practical knowledge of GIS, sensor data, and spatial analysis with current topics such as the urban climate and climate adaptation. This course is designed for professionals who want to look beyond standard weather data and specifically need local, high-resolution climate information.
A few reasons to choose this course:
With this course, you will not only develop technical skills but also gain a better understanding of how crowdsourced meteorological data and personal weather stations can contribute to a climate-resilient living environment.
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Contact usThe first day of the course focuses on the fundamentals of urban climate research and the use of personal weather stations. Participants will learn about the role of sensor networks and crowdsourced meteorological data in analyzing microclimates in urban areas. The course will cover the different types of weather stations and IoT sensors and how they collect data on temperature, humidity, wind, and precipitation.
In addition, the course discusses how these sensors are part of larger networks and online platforms where weather data is made available. Participants learn how this data can be obtained, prepared, and imported into a GIS environment. Attention is also given to an important aspect of sensor data: data quality. Because personal weather stations are not always installed according to meteorological standards, participants learn how to identify anomalies and how to perform simple quality checks.
By the end of the day, participants will have an understanding of the origins of weather station data, the potential of crowdsourced data, and the first steps to using this data in spatial analyses.
The second day of the course focuses on analyzing and visualizing weather station data. Participants will use GIS to convert data points from personal weather stations into spatial maps and analyses. In doing so, they will learn how to visualize temperature and other meteorological variables and how to identify spatial patterns within cities.
Techniques such as interpolation are used to create temperature maps based on scattered measurement points. Attention is also given to combining weather station data with other geodata, such as land use, building density, and green infrastructure. This allows participants to establish connections between urban design and local climate effects.
Finally, we will discuss how these analyses can be applied in the practice of climate adaptation and urban planning. Participants will see how microclimate data can be used to map heat stress, analyze the impact of urban green spaces, and support data-driven decisions within smart city and climate projects.
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Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic understanding of GIS is helpful for this course, but not strictly necessary. The key concepts will be explained during the course. Some experience with geodata or mapping software such as QGIS can help you get started with the exercises more quickly.
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Personal weather stations are compact sensors installed by individuals or organizations to measure local weather conditions. The data from these stations is often shared via online platforms and serves as an important source of crowdsourced meteorological data. In this course, you will learn how to assess the quality of this data and how to identify anomalous measurements.
During the course, students will use GIS software to analyze and visualize weather station data. Among other tools, QGIS will be used for spatial analyses and map visualizations of microclimates and temperature patterns in urban areas.
Afterward, you can use data from personal weather stations to analyze microclimates, create temperature maps, and identify urban climate impacts. This knowledge can be applied in climate research, smart city projects, urban planning, and climate adaptation.
