Towards an Inclusive Urban Environment: A Participatory Approach for Collecting Spatial Accessibility Data in Zurich (Short Paper)

The unprecedented rate of urbanization, along with the increase in the aging and disabled populations, bring about an increasing demand for public services and an inclusive urban environment that allows easy access to those facilities. Spatial Accessibility is a measure to assess how inclusive a city is and how easily public facilities can be reached from a specific location through movement in physical space or built environment. A detailed geodata source of accessibility features is needed for reliable spatial accessibility assessment, such as sidewalk width, surface type, and incline. However, such data are not readily available due to the huge implication costs. Remote crowdsourcing data collection using Street View Imagery, so-called \u27virtual audits\u27 have been introduced as a valid, cost-efficient tool for accessibility data enrichment at scales compared to conventional methods because it enables involving more participants, saving more time by avoiding field visits and covering a larger area. Therefore, in our pilot project, ZuriACT: Zurich Accessible CiTy, with the help of digital tools that allow for virtual inspections and measurements of accessibility features, we want to contribute to collecting and enriching accessibility information in the city of Zurich embedded in a citizen science project that will have both scientific and social impacts. With the help of additional accessibility data produced in this project, the issues of an inclusive urban environment can be demonstrated by mapping the potential spatial inequalities in access to public facilities for disabled or restricted people in terms of mobility. Thus, this project provides helpful insight into implementing policy interventions for overcoming accessibility biases to ensure equitable services, particularly for people with disabilities, and contributes to creating an inclusive and sustainable urban environment. It goes without saying that an inclusive city is beneficial and impacts the quality of life of not only the population groups mentioned above but also the society at large

Towards an inclusive urban environment: A participatory approach for collecting spatial accessibility data in Zurich (Short Paper)

The unprecedented rate of urbanization, along with the increase in the aging and disabled populations, bring about an increasing demand for public services and an inclusive urban environment that allows easy access to those facilities. Spatial Accessibility is a measure to assess how inclusive a city is and how easily public facilities can be reached from a specific location through movement in physical space or built environment. A detailed geodata source of accessibility features is needed for reliable spatial accessibility assessment, such as sidewalk width, surface type, and incline. However, such data are not readily available due to the huge implication costs. Remote crowdsourcing data collection using Street View Imagery, so-called 'virtual audits' have been introduced as a valid, cost-efficient tool for accessibility data enrichment at scales compared to conventional methods because it enables involving more participants, saving more time by avoiding field visits and covering a larger area. Therefore, in our pilot project, ZuriACT: Zurich Accessible CiTy, with the help of digital tools that allow for virtual inspections and measurements of accessibility features, we want to contribute to collecting and enriching accessibility information in the city of Zurich embedded in a citizen science project that will have both scientific and social impacts. With the help of additional accessibility data produced in this project, the issues of an inclusive urban environment can be demonstrated by mapping the potential spatial inequalities in access to public facilities for disabled or restricted people in terms of mobility. Thus, this project provides helpful insight into implementing policy interventions for overcoming accessibility biases to ensure equitable services, particularly for people with disabilities, and contributes to creating an inclusive and sustainable urban environment. It goes without saying that an inclusive city is beneficial and impacts the quality of life of not only the population groups mentioned above but also the society at large

Navigation challenges in urban areas for persons with mobility restrictions

The Sustainable Development Goals (SDGs) promote making the world better for everyone, with a focus on creating cities that are inclusive and sustainable, as outlined in SDG 11. Spatial accessibility plays a pivotal role in fostering age-friendly and inclusive urban environments. However, there is still a lack of complete data on accessibility essential for providing mobility services to individuals with restricted mobility, mainly due to the high costs. While some participatory initiatives like OpenStreetMap (OSM) have made progress in this area, there is still a significant gap in data about sidewalk accessibility. To address this gap, we used a citizen science approach to gather information and improve our understanding of sidewalk accessibility in District 1 of Zurich. Eighteen individuals from diverse population groups took part in our study. Using the Project Sidewalk web tool (PRSW), participants collected sidewalk features like curb ramps and surface problems by virtually inspecting street view images. In this paper, we present preliminary results derived from participatory data collection. The findings show the variances in accessibility labels concerning their frequency, spatial distribution, and severity levels attributed by participants. Furthermore, we provide insights into the accuracy of the data, verified through validation by experts in geographical knowledge using PRSW. Our approach allowed for broader participation and diverse perspectives in collecting sidewalk accessibility data. We believe that the provided dataset has the potential to address unanswered questions about spatial accessibility. For instance, the distribution of accessibility within specific population groups or across a city can be explored. This information can help policymakers develop interventions that tackle accessibility inequalities and ensure equitable access, especially for those with mobility impairments

The impact of microscale street elements on active transport of mobility-restricted individuals: A systematic review

Background Ensuring adequate spatial accessibility to diverse facilities is essential to shaping sustainable, inclusive cities and increasing the well-being of citizens. However, mobility-restricted individuals are found to have lower spatial accessibility in urban areas due to contextual factors, such as physical barriers. Most accessibility assessments focus on the general population and use the road network, due to the lack of data on how contextual factors influence different individuals and lack of geographical data representing pedestrian infrastructure or accessibility features. Methods We searched three databases: Scopus, Web of Science and PubMed. After filtering for eligibility, we examined the remaining 20 studies to understand the perceptions mobility-restricted individuals (i.e., mobility-impaired, visually impaired, or older adults) have of microscale street elements present in urban environments. Data regarding target population groups, data collection methods used and mentioned street elements were extracted. Results The included works tend to focus on a single population group, and disregard within-group differences regarding mobility capacities. Qualitative methods, such as interviews, focus groups and questionnaires, are most frequently used to investigate individual perceptions. To a great extent, individuals perceive microscale street elements differently depending on their mobility capacities. Even if partial overlap exists between population groups, when considering the frequency with which the street elements were mentioned, the impact on their individual accessibility may differ drastically. Certain street elements were highlighted, which can have a twofold effect, acting as barriers for some individuals and facilitators for others. Conclusion The results show that contradicting needs stemming from between- and within-group heterogeneities, together with the highly contextual character of spatial accessibility, pose extensive challenges for designing a barrier-free space for everyone. The results of this review provide a basis for urban planners and policymakers to design the urban infrastructure from a more inclusive perspective, based on comprehensive knowledge

Spatial accessibility assessment of homecare workers to the older population in the city of Zurich

Access to homecare is paramount for the older population wishing not to move to retirement homes. In Zurich, the organization “Spitex Zürich” is the public homecare provider, allowing the older population to experience high-quality life at home through regular visits. A major factor for ensuring self-determined living for the older population consists of a detailed understanding of the spatial accessibility to Spitex locations. Therefore, this paper is concerned with applying four different FCA methods for assessing the spatial accessibility of Spitex homecare workers to people aged 65 + in Zurich through the road and bike networks. The modified-Huff-model-three-step-FCA method is found to be best suited for modeling potential spatial accessibility in Zurich. Spatial accessibility index values show similar geographical distributions regardless of transportation mode. The neighborhoods of Seebach and Hottingen are identified as regions with relatively low spatial accessibility. Our findings contribute to a better understanding of the interpretation and the measurement of spatial accessibility, thereby facilitating autonomous living for the older population residing in Zurich, ultimately increasing the inclusiveness of the city

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.Peer reviewe

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.Peer reviewe

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review

Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings.Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review.Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods.Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework