R.F.L. Teeuwen
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11 records found
1
Conference paper
(2025)
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Flavia Lopes, R.F.L. Teeuwen, Susan Ruinaard, Kristin Åkerlund, Tristan Bridge, Jorge Gil
To facilitate a healthy lifestyle and living environment, m unicipalities must develop effective plans to provide their population with access to activities linked to health and well - being , such as sports and outdoor recreation , via sustainable travel modes . However, accessibility metrics are note widely ado pted in practice , and focused on such activities remains lacking . This study introduces a sustainable accessibility index to everyday sports and outdoor activity - locations as a planning and decision support tool for municipalities . The index offers a synthesis combining travel time and opportunities in a single accessibility metric that oppos ing sustainable mode s against car travel , to facilitate its adoption. Additionally, it implements an interactive mapping platform that presents the results of the accessibility index to municipalities and sports and outdoors organizations in Sweden at various scales , covering the entire country
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To facilitate a healthy lifestyle and living environment, m unicipalities must develop effective plans to provide their population with access to activities linked to health and well - being , such as sports and outdoor recreation , via sustainable travel modes . However, accessibility metrics are note widely ado pted in practice , and focused on such activities remains lacking . This study introduces a sustainable accessibility index to everyday sports and outdoor activity - locations as a planning and decision support tool for municipalities . The index offers a synthesis combining travel time and opportunities in a single accessibility metric that oppos ing sustainable mode s against car travel , to facilitate its adoption. Additionally, it implements an interactive mapping platform that presents the results of the accessibility index to municipalities and sports and outdoors organizations in Sweden at various scales , covering the entire country
Journal article
(2025)
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Zhiyang Wang, Sari Aaltonen, More Authors..., Roos Teeuwen, Vasileios Milias, Carmen Peuters, Bruno Raimbault, Teemu Palviainen, Erin Lumpe, Achilleas Psyllidis, Jaakko Kaprio
Under the exposome framework, this study examined the relationship between the urban physical environment and leisure-time physical activity during early midlife based on 394 participants (mean age: 37, range 34–40) from the FinnTwin12 cohort, residing in five major Finnish cities in 2020. We curated 145 urban physical exposures based on residential addresses and measured three outcomes: total leisure-time physical activity (total LTPA) and two sub-domains: leisure-time physical activity without commuting activity (LTPA) and commuting activity. K-prototypes clustering identified three urban clusters: “original city center,” “new city center,” and “suburban,” each with distinct environmental patterns. Regression models showed that participants in the “suburban” cluster had lower levels of total LTPA and LTPA compared to those in the “original city center” cluster, while we found null findings for commuting activity. Then, repeated regression models with a p-value threshold of 0.01 were used to initially select candidates. eXtreme Gradient Boosting models identified greenspaces and road characteristics as the top important factors influencing total LTPA, while pocket park and greenness were ranked as the top important factors influencing LTPA. The relationships were non-linear. There were thresholds for the count and size of pocket parks within 800 m walking distance and the modified soil adjusted vegetation index, determining whether they positively or negatively predict LTPA. Our findings suggested that the urban environment in Finnish cities was associated with leisure-time physical activity, which revealed new residential pattern and identified key exposures of road, pocket park, and greenness with non-linear effect, that can guide future policies.
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Under the exposome framework, this study examined the relationship between the urban physical environment and leisure-time physical activity during early midlife based on 394 participants (mean age: 37, range 34–40) from the FinnTwin12 cohort, residing in five major Finnish cities in 2020. We curated 145 urban physical exposures based on residential addresses and measured three outcomes: total leisure-time physical activity (total LTPA) and two sub-domains: leisure-time physical activity without commuting activity (LTPA) and commuting activity. K-prototypes clustering identified three urban clusters: “original city center,” “new city center,” and “suburban,” each with distinct environmental patterns. Regression models showed that participants in the “suburban” cluster had lower levels of total LTPA and LTPA compared to those in the “original city center” cluster, while we found null findings for commuting activity. Then, repeated regression models with a p-value threshold of 0.01 were used to initially select candidates. eXtreme Gradient Boosting models identified greenspaces and road characteristics as the top important factors influencing total LTPA, while pocket park and greenness were ranked as the top important factors influencing LTPA. The relationships were non-linear. There were thresholds for the count and size of pocket parks within 800 m walking distance and the modified soil adjusted vegetation index, determining whether they positively or negatively predict LTPA. Our findings suggested that the urban environment in Finnish cities was associated with leisure-time physical activity, which revealed new residential pattern and identified key exposures of road, pocket park, and greenness with non-linear effect, that can guide future policies.
Abstract
(2025)
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R.F.L. Teeuwen, Jorge Gil
Empirical traffic flow data are key in transport, mobility, and spatial planning. They can help understand congestion, traffic safety, environmental exposure and risks, among others. At scale, they can support data-driven decision making, helping to decide where interventions are most needed.
However, existing traffic flow data from sensors or traffic counts [1] lack spatio-temporal coverage and granularity. Other data, e.g. from navigation API’s, are proprietary, commercial or limited-access, and unavailable to decision-makers. Large mobile phone traces data recently emerged as a promising source to capture dynamics at scale given their size, granularity, and coverage. They have been used to analyse travel demand (origin-
destination), activity-locations, and individuals’ activity spaces. Yet, despite their potential for exploring trajectories and traffic flows [1], dynamic applications other than understanding pedestrian routing behaviour [2] remain unexplored.
This study aims to explore how traffic flows with high spatial and temporal coverage and granularity can be estimated from vehicle trajectories based on sparse mobile phone geolocation data. We develop a methodology to create trajectories and flows from raw location data and test how various parameters affect the results. We contribute our methodology, code and data to allow for replication in other studies, and reflect on directions for future development. ...
However, existing traffic flow data from sensors or traffic counts [1] lack spatio-temporal coverage and granularity. Other data, e.g. from navigation API’s, are proprietary, commercial or limited-access, and unavailable to decision-makers. Large mobile phone traces data recently emerged as a promising source to capture dynamics at scale given their size, granularity, and coverage. They have been used to analyse travel demand (origin-
destination), activity-locations, and individuals’ activity spaces. Yet, despite their potential for exploring trajectories and traffic flows [1], dynamic applications other than understanding pedestrian routing behaviour [2] remain unexplored.
This study aims to explore how traffic flows with high spatial and temporal coverage and granularity can be estimated from vehicle trajectories based on sparse mobile phone geolocation data. We develop a methodology to create trajectories and flows from raw location data and test how various parameters affect the results. We contribute our methodology, code and data to allow for replication in other studies, and reflect on directions for future development. ...
Empirical traffic flow data are key in transport, mobility, and spatial planning. They can help understand congestion, traffic safety, environmental exposure and risks, among others. At scale, they can support data-driven decision making, helping to decide where interventions are most needed.
However, existing traffic flow data from sensors or traffic counts [1] lack spatio-temporal coverage and granularity. Other data, e.g. from navigation API’s, are proprietary, commercial or limited-access, and unavailable to decision-makers. Large mobile phone traces data recently emerged as a promising source to capture dynamics at scale given their size, granularity, and coverage. They have been used to analyse travel demand (origin-
destination), activity-locations, and individuals’ activity spaces. Yet, despite their potential for exploring trajectories and traffic flows [1], dynamic applications other than understanding pedestrian routing behaviour [2] remain unexplored.
This study aims to explore how traffic flows with high spatial and temporal coverage and granularity can be estimated from vehicle trajectories based on sparse mobile phone geolocation data. We develop a methodology to create trajectories and flows from raw location data and test how various parameters affect the results. We contribute our methodology, code and data to allow for replication in other studies, and reflect on directions for future development.
However, existing traffic flow data from sensors or traffic counts [1] lack spatio-temporal coverage and granularity. Other data, e.g. from navigation API’s, are proprietary, commercial or limited-access, and unavailable to decision-makers. Large mobile phone traces data recently emerged as a promising source to capture dynamics at scale given their size, granularity, and coverage. They have been used to analyse travel demand (origin-
destination), activity-locations, and individuals’ activity spaces. Yet, despite their potential for exploring trajectories and traffic flows [1], dynamic applications other than understanding pedestrian routing behaviour [2] remain unexplored.
This study aims to explore how traffic flows with high spatial and temporal coverage and granularity can be estimated from vehicle trajectories based on sparse mobile phone geolocation data. We develop a methodology to create trajectories and flows from raw location data and test how various parameters affect the results. We contribute our methodology, code and data to allow for replication in other studies, and reflect on directions for future development.
In this dissertation, we propose innovative approaches for assessing urban greenspace accessibility with a specific focus on factors affecting children’s access. Access to quality greenspaces in urban areas is crucial for fostering the health and well-being of children and providing spaces for recreation, socialization, and personal development. Unlike adults', children’s access to greenspaces is influenced by factors such as their daily activity patterns, levels of autonomy, and various physical and perceived barriers. While many studies in epidemiology, spatial equity, and urban planning aim to evaluate access to greenspaces for children across different urban scales and geographical contexts, they often utilize methods designed for the general population, overlooking the distinct factors affecting children's access. Our research addresses this gap by developing tailored methodologies that account for the specific needs and experiences of children in urban environments.
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In this dissertation, we propose innovative approaches for assessing urban greenspace accessibility with a specific focus on factors affecting children’s access. Access to quality greenspaces in urban areas is crucial for fostering the health and well-being of children and providing spaces for recreation, socialization, and personal development. Unlike adults', children’s access to greenspaces is influenced by factors such as their daily activity patterns, levels of autonomy, and various physical and perceived barriers. While many studies in epidemiology, spatial equity, and urban planning aim to evaluate access to greenspaces for children across different urban scales and geographical contexts, they often utilize methods designed for the general population, overlooking the distinct factors affecting children's access. Our research addresses this gap by developing tailored methodologies that account for the specific needs and experiences of children in urban environments.
Children’s access to urban greenspace
A survey of factors and measures
Access to greenspace impacts children’s physical, social, and mental health. Numerous factors affect children’s access to urban greenspaces, often distinct from those affecting the general population, including parental restrictions, limited routine activity-space, and particular preferences. Most accessibility measures, however, employ the same principles for children and the general population, and a comprehensive exploration of factors and corresponding measures remains lacking. We conduct a scoping review and workshops with researchers and practitioners to identify factors affecting children’s access to greenspace, synthesize them into a conceptual model, and assess how existing accessibility measures address these factors. We focus on children aged 6–11 years old. Our analysis indicates children’s access involves a trade-off between reachability, determined by the route connecting the child’s starting setting to greenspace, and attractiveness, determined by how the greenspaces adhere to the child’s, and their companions’, preferences and motivations for visiting. Safety perceptions are important throughout. Existing accessibility measures predominantly emphasize reachability, neglecting personal characteristics and motivations. Based on our findings, we propose future directions for developing child-centered accessibility metrics. Our overview of metrics can facilitate decision-making in the selection of suitable measures, while our conceptual model can foster shared understanding of factors affecting children’s access to urban greenspace.
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Access to greenspace impacts children’s physical, social, and mental health. Numerous factors affect children’s access to urban greenspaces, often distinct from those affecting the general population, including parental restrictions, limited routine activity-space, and particular preferences. Most accessibility measures, however, employ the same principles for children and the general population, and a comprehensive exploration of factors and corresponding measures remains lacking. We conduct a scoping review and workshops with researchers and practitioners to identify factors affecting children’s access to greenspace, synthesize them into a conceptual model, and assess how existing accessibility measures address these factors. We focus on children aged 6–11 years old. Our analysis indicates children’s access involves a trade-off between reachability, determined by the route connecting the child’s starting setting to greenspace, and attractiveness, determined by how the greenspaces adhere to the child’s, and their companions’, preferences and motivations for visiting. Safety perceptions are important throughout. Existing accessibility measures predominantly emphasize reachability, neglecting personal characteristics and motivations. Based on our findings, we propose future directions for developing child-centered accessibility metrics. Our overview of metrics can facilitate decision-making in the selection of suitable measures, while our conceptual model can foster shared understanding of factors affecting children’s access to urban greenspace.
The configuration of public open spaces plays a crucial role in shaping how different people use them. Nevertheless, our understanding of how the physical features of public open spaces influence the activities conducted within them, and the extent to which this impact differs across various individuals and population groups, is currently limited. In this study, we explore how the physical characteristics of public open spaces influence the likelihood of use among individuals, spanning different age and gender groups. By employing crowdsourcing, street-level imagery, statistical comparisons, and reflexive thematic analysis we uncover significant variations in the suitability of public open spaces for distinct activities, such as socializing or exercising. Greenspaces emerge as the preferred choice for almost all activities, whereas streets are consistently rated as the least suitable. Additionally, we identified various characteristics that influence the activities people are likely to engage in. These include the size of the space, the presence of seating, natural elements such as vegetation or water bodies, and the proximity to transport infrastructure. Surprisingly, we do not observe statistically significant differences in preferences among most age and gender groups. Overall, our study underscores the need for providing a diverse range of public open spaces tailored to accommodate different individuals, population groups, and activities.
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The configuration of public open spaces plays a crucial role in shaping how different people use them. Nevertheless, our understanding of how the physical features of public open spaces influence the activities conducted within them, and the extent to which this impact differs across various individuals and population groups, is currently limited. In this study, we explore how the physical characteristics of public open spaces influence the likelihood of use among individuals, spanning different age and gender groups. By employing crowdsourcing, street-level imagery, statistical comparisons, and reflexive thematic analysis we uncover significant variations in the suitability of public open spaces for distinct activities, such as socializing or exercising. Greenspaces emerge as the preferred choice for almost all activities, whereas streets are consistently rated as the least suitable. Additionally, we identified various characteristics that influence the activities people are likely to engage in. These include the size of the space, the presence of seating, natural elements such as vegetation or water bodies, and the proximity to transport infrastructure. Surprisingly, we do not observe statistically significant differences in preferences among most age and gender groups. Overall, our study underscores the need for providing a diverse range of public open spaces tailored to accommodate different individuals, population groups, and activities.
The study of urban greenspaces typically relies on three types of data: people’s subjective perceptions collected via questionnaires, vegetation indices derived from satellite imagery, such as the Normalized Difference Vegetation Index (NDVI), and Land Use or Land Cover maps, such as OpenStreetMap (OSM). Data on people’s perceptions are essential when researching human activities, yet they scale poorly. NDVI and OSM data, on the other hand, are freely available worldwide, thus valuable for assessing cities at scale or prioritizing locations for interventions. However, it is unclear how effectively NDVI and OSM data capture people’s visual perceptions of urban greenspaces. In this work, we collect people’s visual perceptions of public spaces in three major European cities through crowdsourcing, quantitatively compare them to NDVI and OSM data, and qualitatively investigate disparities. We found that NDVI moderately correlates with perceived greenness and that not only OSM greenspaces but also pocket parks and play spaces are often considered green. Furthermore, we found that people’s perceptions correspond best to OSM data in small radius distances and NDVI data in larger radius distances and that combining NDVI and OSM data can improve identification of places in OSM that are commonly considered green. Our qualitative analysis revealed that configuration and variety of vegetation, and presence of other natural or built-up features, influence people’s perceptions of greenspace. With our findings we aim to help researchers and practitioners make more informed decisions when collecting greenspace data for their specific context, ultimately contributing to green urban environments that reflect people’s perspectives.
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The study of urban greenspaces typically relies on three types of data: people’s subjective perceptions collected via questionnaires, vegetation indices derived from satellite imagery, such as the Normalized Difference Vegetation Index (NDVI), and Land Use or Land Cover maps, such as OpenStreetMap (OSM). Data on people’s perceptions are essential when researching human activities, yet they scale poorly. NDVI and OSM data, on the other hand, are freely available worldwide, thus valuable for assessing cities at scale or prioritizing locations for interventions. However, it is unclear how effectively NDVI and OSM data capture people’s visual perceptions of urban greenspaces. In this work, we collect people’s visual perceptions of public spaces in three major European cities through crowdsourcing, quantitatively compare them to NDVI and OSM data, and qualitatively investigate disparities. We found that NDVI moderately correlates with perceived greenness and that not only OSM greenspaces but also pocket parks and play spaces are often considered green. Furthermore, we found that people’s perceptions correspond best to OSM data in small radius distances and NDVI data in larger radius distances and that combining NDVI and OSM data can improve identification of places in OSM that are commonly considered green. Our qualitative analysis revealed that configuration and variety of vegetation, and presence of other natural or built-up features, influence people’s perceptions of greenspace. With our findings we aim to help researchers and practitioners make more informed decisions when collecting greenspace data for their specific context, ultimately contributing to green urban environments that reflect people’s perspectives.
Recent evidence underscores the importance of greenspace exposure in promoting physical activity, and in having a positive impact on mental health and cognitive development. Accessibility has been identified to be the primary motivating factor when it comes to encouraging greenspace use and, correspondingly, exposure. Existing quantitative approaches to measuring greenspace accessibility predominantly focus on the areas surrounding home locations, often disregarding access from other settings such as schools or workplaces, exposures while on the move, and mobility differences among different population age groups. This article introduces a novel method to measure greenspace accessibility that considers access from different activity settings (i.e., homes, schools, and the commutes between them) for children and adolescents, while accounting for the dependency of human access on the road network. We use Amsterdam, Rotterdam, and The Hague in the Netherlands as case studies to illustrate the utility of our method. Compared to conventional measures of greenspace accessibility, we show that accounting for school and commuting settings, in addition to residences, captures previously untapped accessibility aspects for both children and adolescents. Our approach can be replicated in other cities worldwide, with the aspiration to provide planners and public health policy-makers with a methodological tool that can help in evaluating access and use of greenspaces when designing health-promoting interventions.
...
Recent evidence underscores the importance of greenspace exposure in promoting physical activity, and in having a positive impact on mental health and cognitive development. Accessibility has been identified to be the primary motivating factor when it comes to encouraging greenspace use and, correspondingly, exposure. Existing quantitative approaches to measuring greenspace accessibility predominantly focus on the areas surrounding home locations, often disregarding access from other settings such as schools or workplaces, exposures while on the move, and mobility differences among different population age groups. This article introduces a novel method to measure greenspace accessibility that considers access from different activity settings (i.e., homes, schools, and the commutes between them) for children and adolescents, while accounting for the dependency of human access on the road network. We use Amsterdam, Rotterdam, and The Hague in the Netherlands as case studies to illustrate the utility of our method. Compared to conventional measures of greenspace accessibility, we show that accounting for school and commuting settings, in addition to residences, captures previously untapped accessibility aspects for both children and adolescents. Our approach can be replicated in other cities worldwide, with the aspiration to provide planners and public health policy-makers with a methodological tool that can help in evaluating access and use of greenspaces when designing health-promoting interventions.
Accessible outdoor spaces for unsupervised play are important for children’s health. However, parents impose constraints based on their perception of safety, which can have a significant impact on which play spaces are actually accessible to children. Such constraints are not taken into account by widely adopted accessibility indicators that use generic radial buffers or travel distances. We introduce a child’s play accessibility metric, which measures the ease with which children can reach outdoor play spaces without supervision. We developed this metric through an iterative co-design process with experts on the built environment and children’s health, leveraging open data. Our metric considers traffic, natural barriers to children, and a range of playable spaces. It can be used by planners and policymakers to enable large-scale assessments of play space accessibility, identify associated equity issues, and benchmark progress toward healthier environments for all ages.
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Accessible outdoor spaces for unsupervised play are important for children’s health. However, parents impose constraints based on their perception of safety, which can have a significant impact on which play spaces are actually accessible to children. Such constraints are not taken into account by widely adopted accessibility indicators that use generic radial buffers or travel distances. We introduce a child’s play accessibility metric, which measures the ease with which children can reach outdoor play spaces without supervision. We developed this metric through an iterative co-design process with experts on the built environment and children’s health, leveraging open data. Our metric considers traffic, natural barriers to children, and a range of playable spaces. It can be used by planners and policymakers to enable large-scale assessments of play space accessibility, identify associated equity issues, and benchmark progress toward healthier environments for all ages.
Journal article
(2021)
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A. Psyllidis, Fábio Duarte, R.F.L. Teeuwen, Arianna Salazar Miranda, Tom Benson, A. Bozzon
As cities resume life in public space, they face the difficult task of retaining outdoor activity while decreasing exposure to airborne viruses, such as the novel coronavirus. Even though the transmission risk is higher in indoor spaces, recent evidence suggests that physical contact outdoors also contributes to an increased virus exposure. Given that streets constitute the largest percentage of public space in cities, there is an increasing need to prioritise their use to minimise transmission risk. However, city officials currently lack the assessment tools to achieve this. This article evaluates the extent to which street segments are associated with spatiotemporal variations of potential exposures of pedestrians to virus transmission. We develop a multi-component risk score that considers both urban form and human activity along streets over time, including (a) an assessment of pedestrian infrastructure according to the average width of pavements, (b) a measure of accessibility for each street based on its position in the street network, (c) an activity exposure score that identifies places along streets where exposure could be higher and (d) an estimate of the number of pedestrians that will pass through each street during weekdays and weekends. We use Amsterdam in the Netherlands as a case study to illustrate how our score could be used to assess the exposure of pedestrians to virus transmission along streets. Our approach can be replicated in other cities facing a similar challenge of bringing life back to the streets while minimising transmission risks.
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As cities resume life in public space, they face the difficult task of retaining outdoor activity while decreasing exposure to airborne viruses, such as the novel coronavirus. Even though the transmission risk is higher in indoor spaces, recent evidence suggests that physical contact outdoors also contributes to an increased virus exposure. Given that streets constitute the largest percentage of public space in cities, there is an increasing need to prioritise their use to minimise transmission risk. However, city officials currently lack the assessment tools to achieve this. This article evaluates the extent to which street segments are associated with spatiotemporal variations of potential exposures of pedestrians to virus transmission. We develop a multi-component risk score that considers both urban form and human activity along streets over time, including (a) an assessment of pedestrian infrastructure according to the average width of pavements, (b) a measure of accessibility for each street based on its position in the street network, (c) an activity exposure score that identifies places along streets where exposure could be higher and (d) an estimate of the number of pedestrians that will pass through each street during weekdays and weekends. We use Amsterdam in the Netherlands as a case study to illustrate how our score could be used to assess the exposure of pedestrians to virus transmission along streets. Our approach can be replicated in other cities facing a similar challenge of bringing life back to the streets while minimising transmission risks.
Conference paper
(2018)
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Lilia Angelova, Puck Flikweert, Panagiotis Karydakis, Daniël Kersbergen, Roos Teeuwen, Kotryna Valečkaitė, Edward Verbree, Martijn Meijers, Stefan van der Spek
Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an addition to static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy, measuring temperature and humidity. The emphasis of the research is on the localisation of the sensor platforms. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. In this method the GPS module is only used for the composition of the radiomap. The quality of the radiomap methodology was assessed by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size.
...
Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an addition to static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy, measuring temperature and humidity. The emphasis of the research is on the localisation of the sensor platforms. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. In this method the GPS module is only used for the composition of the radiomap. The quality of the radiomap methodology was assessed by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size.