A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap.

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data producers and urban analysts to manage the uneven coverage of OSM data, as well as a framework to support the assessment of completeness biases.

Carbon fluxes related to land use and land cover change in Baden-Württemberg.

Spatially explicit information on carbon fluxes related to land use and land cover change (LULCC) is of value for the implementation of local climate change mitigation strategies. However, estimates of these carbon fluxes are often aggregated to larger areas. We estimated committed gross carbon fluxes related to LULCC in Baden-Württemberg, Germany, using different emission factors. In doing so, we compared four different data sources regarding their suitability for estimating the fluxes: (a) a land cover dataset derived from OpenStreetMap (OSMlanduse); (b) OSMlanduse with removal of sliver polygons (OSMlanduse cleaned), (c) OSMlanduse enhanced with a remote sensing time series analysis (OSMlanduse+); (d) the LULCC product of Landschaftsveränderungsdienst (LaVerDi) from the German Federal Agency of Cartography and Geodesy. We produced a high range of carbon flux estimates, mostly caused by differences in the area of the LULCC detected by the different change methods. Except for the OSMlanduse change method, all LULCC methods achieved results that are comparable to other gross emission estimates. The carbon flux estimates of the most plausible change methods, OSMlanduse cleaned and OSMlanduse+, were 291,710 Mg C yr-1 and 93,591 Mg C yr-1, respectively. Uncertainties were mainly caused by incomplete spatial coverage of OSMlanduse, false positive LULCC due to changes and corrections made in OpenStreetMap during the study period, and a high number of sliver polygons in the OSMlanduse changes. Overall, the results showed that OSM can be successfully used to estimate LULCC carbon fluxes if data preprocessing is performed with the suggested methods.

Initial response to the COVID-19 pandemic on real-life well-being, social contact and roaming behavior in patients with schizophrenia, major depression and healthy controls: A longitudinal ecological momentary assessment study.

The COVID-19 pandemic strongly impacted people's daily lives. However, it remains unknown how the pandemic situation affects daily-life experiences of individuals with preexisting severe mental illnesses (SMI). In this real-life longitudinal study, the acute onset of the COVID-19 pandemic in Germany did not cause the already low everyday well-being of patients with schizophrenia (SZ) or major depression (MDD) to decrease further. On the contrary, healthy participants' well-being, anxiety, social isolation, and mobility worsened, especially in healthy individuals at risk for mental disorder, but remained above the levels seen in patients. Despite being stressful for healthy individuals at risk for mental disorder, the COVID-19 pandemic had little additional influence on daily-life well-being in psychiatric patients with SMI. This highlights the need for preventive action and targeted support of this vulnerable population.

Leveraging OpenStreetMap and Multimodal Remote Sensing Data with Joint Deep Learning for Wastewater Treatment Plants Detection.

Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common approach to securing clean water supplies for this purpose is via wastewater treatment. To date, an effective method of detecting wastewater treatment plants (WWTP) accurately and automatically via remote sensing is unavailable. In this paper, we provide a solution to this task by proposing a novel joint deep learning (JDL) method that consists of a fine-tuned object detection network and a multi-task residual attention network (RAN). By leveraging OpenStreetMap (OSM) and multimodal remote sensing (RS) data, our JDL method is able to simultaneously tackle two different tasks: land use land cover (LULC) and WWTP classification. Moreover, JDL exploits the complementary effects between these tasks for a performance gain. We train JDL using 4,187 WWTP features and 4,200 LULC samples and validate the performance of the proposed method over a selected area around Stuttgart with 723 WWTP features and 1,200 LULC samples to generate an LULC classification map and a WWTP detection map. Extensive experiments conducted with different comparative methods demonstrate the effectiveness and efficiency of our JDL method in automatic WWTP detection in comparison with single-modality/single-task or traditional survey methods. Moreover, lessons learned pave the way for future works to simultaneously and effectively address multiple large-scale mapping tasks (e.g., both mapping LULC and detecting WWTP) from multimodal RS data via deep learning.

Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019.

BACKGROUND: The ability of disaster response, preparedness, and mitigation efforts to assess the loss of physical accessibility to health facilities and to identify impacted populations is key in reducing the humanitarian consequences of disasters. Recent studies use either network- or raster-based approaches to measure accessibility in respect to travel time. Our analysis compares a raster- and a network- based approach that both build on open data with respect to their ability to assess the loss of accessibility due to a severe flood event. As our analysis uses open access data, the approach should be transferable to other flood-prone sites to support decision-makers in the preparation of disaster mitigation and preparedness plans. METHODS: Our study is based on the flood events following Cyclone Idai in Mozambique in 2019 and uses both raster- and network-based approaches to compare accessibility to health sites under normal conditions to the aftermath of the cyclone to assess the loss of accessibility. Part of the assessment is a modified centrality indicator, which identifies the specific use of the road network for the population to reach health facilities. RESULTS: Results for the raster- and the network-based approaches differed by about 300,000 inhabitants (~ 800,000 to ~ 500,000) losing accessibility to healthcare sites. The discrepancy was related to the incomplete mapping of road networks and affected the network-based approach to a higher degree. The modified centrality indicator allowed us to identify road segments that were most likely to suffer from flooding and to highlight potential backup roads in disaster settings. CONCLUSIONS: The different results obtained between the raster- and network-based methods indicate the importance of data quality assessments in addition to accessibility assessments as well as the importance of fostering mapping campaigns in large parts of the Global South. Data quality is therefore a key parameter when deciding which method is best suited for local conditions. Another important aspect is the required spatial resolution of the results. Identification of critical segments of the road network provides essential information to prepare for potential disasters.

The evolution of humanitarian mapping within the OpenStreetMap community.

In the past 10 years, the collaborative maps of OpenStreetMap (OSM) have been used to support humanitarian efforts around the world as well as to fill important data gaps for implementing major development frameworks such as the Sustainable Development Goals. This paper provides a comprehensive assessment of the evolution of humanitarian mapping within the OSM community, seeking to understand the spatial and temporal footprint of these large-scale mapping efforts. The spatio-temporal statistical analysis of OSM's full history since 2008 showed that humanitarian mapping efforts added 60.5 million buildings and 4.5 million roads to the map. Overall, mapping in OSM was strongly biased towards regions with very high Human Development Index. However, humanitarian mapping efforts had a different footprint, predominantly focused on regions with medium and low human development. Despite these efforts, regions with low and medium human development only accounted for 28% of the buildings and 16% of the roads mapped in OSM although they were home to 46% of the global population. Our results highlight the formidable impact of humanitarian mapping efforts such as post-disaster mapping campaigns to improve the spatial coverage of existing open geographic data and maps, but they also reveal the need to address the remaining stark data inequalities, which vary significantly across countries. We conclude with three recommendations directed at the humanitarian mapping community: (1) Improve methods to monitor mapping activity and identify where mapping is needed. (2) Rethink the design of projects which include humanitarian data generation to avoid non-sustainable outcomes. (3) Remove structural barriers to empower local communities and develop capacity.

A neural mechanism for affective well-being: Subgenual cingulate cortex mediates real-life effects of nonexercise activity on energy.

Physical activity substantially improves well-being and mental health, but the underlying brain processes remain unclear. Most research concerns exercise, although the majority of everyday human behaviors, such as walking or stair climbing, are nonexercise activities. Combining neuroimaging with ecological assessment of activity and GPS-triggered smartphone diaries, we show a specific association of nonexercise activity with energy in two independent samples mediated by the subgenual part of the anterior cingulate cortex (sgACC), a key emotion regulatory site. Furthermore, energy predicted a range of mental health metrics. sgACC volume moderated humans' emotional gain from nonexercise activity in real life: Individuals with low sgACC volume, a risk factor for depression, felt less energized when inactive but benefited more from periods of high nonexercise activity. This suggests an everyday life mechanism affecting affective well-being in the general population and, if substantiated in patient samples, a risk and resilience process for mood disorders.

Mapping physical access to healthcare for older adults in sub-Saharan Africa: A cross-sectional analysis with implications for the COVID-19 response.

BACKGROUND: SARS-CoV-2, the virus causing coronavirus disease 2019 (COVID-19), is rapidly spreading across sub-Saharan Africa (SSA). Hospital-based care for COVID-19 is particularly often needed among older adults. However, a key barrier to accessing hospital care in SSA is travel time to the healthcare facility. To inform the geographic targeting of additional healthcare resources, this study aimed to determine the estimated travel time at a 1km × 1km resolution to the nearest hospital and to the nearest healthcare facility of any type for adults aged 60 years and older in SSA. METHODS: We assembled a unique dataset on healthcare facilities' geolocation, separately for hospitals and any type of healthcare facility (including primary care facilities) and including both private- and public-sector facilities, using data from the OpenStreetMap project and the KEMRI Wellcome Trust Programme. Population data at a 1km × 1km resolution was obtained from WorldPop. We estimated travel time to the nearest healthcare facility for each 1km × 1km grid using a cost-distance algorithm. FINDINGS: 9.6% (95% CI: 5.2% - 16.9%) of adults aged ≥60 years had an estimated travel time to the nearest hospital of longer than six hours, varying from 0.0% (95% CI: 0.0% - 3.7%) in Burundi and The Gambia, to 40.9% (95% CI: 31.8% - 50.7%) in Sudan. 11.2% (95% CI: 6.4% - 18.9%) of adults aged ≥60 years had an estimated travel time to the nearest healthcare facility of any type (whether primary or secondary/tertiary care) of longer than three hours, with a range of 0.1% (95% CI: 0.0% - 3.8%) in Burundi to 55.5% (95% CI: 52.8% - 64.9%) in Sudan. Most countries in SSA contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of more than 12 hours and to the nearest healthcare facility of any type of more than six hours. The median travel time to the nearest hospital for the fifth of adults aged ≥60 years with the longest travel times was 348 minutes (equal to 5.8 hours; IQR: 240 - 576 minutes) for the entire SSA population, ranging from 41 minutes (IQR: 34 - 54 minutes) in Burundi to 1,655 minutes (equal to 27.6 hours; IQR: 1065 - 2440 minutes) in Gabon. INTERPRETATION: Our high-resolution maps of estimated travel times to both hospitals and healthcare facilities of any type can be used by policymakers and non-governmental organizations to help target additional healthcare resources, such as new make-shift hospitals or transport programs to existing healthcare facilities, to older adults with the least physical access to care. In addition, this analysis shows precisely where population groups are located that are particularly likely to under-report COVID-19 symptoms because of low physical access to healthcare facilities. Beyond the COVID-19 response, this study can inform countries' efforts to improve care for conditions that are common among older adults, such as chronic non-communicable diseases.

Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2, the virus causing COVID-19, is rapidly spreading across sub-Saharan Africa. Hospital-based care for COVID-19 is often needed, particularly among older adults. However, a key barrier to accessing hospital care in sub-Saharan Africa is travel time to the nearest health-care facility. To inform the geographical targeting of additional health-care resources, we aimed to estimate travel time at a 1 km × 1 km resolution to the nearest hospital and to the nearest health-care facility of any type for adults aged 60 years and older in sub-Saharan Africa. METHODS: We assembled a dataset on the geolocation of health-care facilities, separately for hospitals and any type of health-care facility and including both private-sector and public-sector facilities, using data from the OpenStreetMap project and the Kenya Medical Research Institute-Wellcome Trust Programme. Population data at a 1 km × 1 km resolution were obtained from WorldPop. We estimated travel time to the nearest health-care facility for each 1 km × 1 km grid using a cost-distance algorithm. FINDINGS: 9·6% (95% CI 5·2-16·9) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time to the nearest hospital of 6 h or longer, varying from 0·0% (0·0-3·7) in Burundi and The Gambia to 40·9% (31·8-50·7) in Sudan. For the nearest health-care facility of any type (whether primary, secondary, or tertiary care), 15·9% (95% CI 10·1-24·4) of adults aged 60 years or older across sub-Saharan Africa had an estimated travel time of 2 h or longer, ranging from 0·4% (0·0-4·4) in Burundi to 59·4% (50·1-69·0) in Sudan. Most countries in sub-Saharan Africa contained populated areas in which adults aged 60 years and older had a travel time to the nearest hospital of 12 h or longer and to the nearest health-care facility of any type of 6 h or longer. The median travel time to the nearest hospital for the fifth of adults aged 60 years or older with the longest travel times was 348 min (IQR 240-576; equal to 5·8 h) for the entire population of sub-Saharan Africa, ranging from 41 min (34-54) in Burundi to 1655 min (1065-2440; equal to 27·6 h) in Gabon. INTERPRETATION: Our high-resolution maps of estimated travel times to both hospitals and health-care facilities of any type can be used by policy makers and non-governmental organisations to help target additional health-care resources, such as makeshift hospitals or transport programmes to existing health-care facilities, to older adults with the least physical access to care. In addition, this analysis shows the locations of population groups most likely to under-report COVID-19 symptoms because of low physical access to health-care facilities. Beyond the COVID-19 response, this study can inform the efforts of countries to improve physical access to care for conditions that are common among older adults in the region, such as chronic non-communicable diseases. FUNDING: Bill & Melinda Gates Foundation.

Relationships between incidental physical activity, exercise, and sports with subsequent mood in adolescents.

Physical activity is beneficial for human physical health and well-being. Accordingly, the association between physical activity and mood in everyday life has been a subject of several Ambulatory Assessment studies. This mechanism has been studied in children, adults, and the elderly, but neglected in adolescents. It is critical to examine this mechanism in adolescents because adolescence plays a key role in human development and adolescents' physical activity behavior translates into their behavior in adulthood. We investigated adolescents' mood in relation to distinct physical activities: incidental activity such as climbing stairs; exercise activity, such as skating; and sports, such as playing soccer. We equipped 134 adolescents aged 12-17 years with accelerometers and GPS-triggered electronic diaries to use in their everyday life. Adolescents reported on mood repeatedly in real time across 7 days, and these data were analyzed using multilevel-modeling. After incidental activity, adolescents felt better and more energized. After exercise, adolescents felt better but less calm. After sports, adolescents felt less energized. Analyses of the time course of the effects confirmed our findings. Physical activity influences mood in adolescents' everyday life, but has distinct effects depending on the kind of physical activity. Our results suggest incidental and exercise activities entail higher post-bout valence compared to sports in competitive settings. These findings may serve as an important empirical basis for the targeted application of distinct physical activities to foster well-being in adolescence.

Studying the impact of built environments on human mental health in everyday life: methodological developments, state-of-the-art and technological frontiers.

Rapid worldwide urbanization benefits humans in many aspects, but the prevalence of common psychiatric disorders is increased in urban populations. While the impact of city living and urban upbringing on mental health is well established, it remains elusive which of the multiple factors of urban living convey risk and resilience for mental disorders. For example, air pollutants, traffic noises and fragmented social networks are some of the highly interdependent and complex influences of city living suggested to be detrimental for mental health. In contrast, urban green spaces, social contacts and physical activity have been associated with increased well-being. Knowledge on underlying mechanisms of these associations is crucial for both city planning and healthcare as it informs on how to build environments and to intervene in a way that fosters mental health yet reduces psychiatric disorders. Thus, real-life studies in urban contexts have been launched making use of recent methodological advancements: Mobile devices (e.g. smartphones) to gather intensive longitudinal mental health data, stationary sensor output providing specific context information (e.g. on weather conditions and air pollution), combinations with traditional and modern neuroimaging techniques (e.g. functional near-infrared spectroscopy and portable magnetic-encephalogram caps) and modern virtual reality setups allowing for increasingly realistic and ecological valid simulation of complex urban environments. Here we review selected methodological developments, state-of-the-art approaches as well as technological frontiers and provide examples for their application, highlighting promising potential of these novel methods for tackling the urgent urbanicity societal issue of the 21st century with a view to improve urban contexts conducive to mental health.

Neural correlates of individual differences in affective benefit of real-life urban green space exposure.

Psychiatric morbidity is high in cities, so identifying potential modifiable urban protective factors is important. We show that exposure to urban green space improves well-being in naturally behaving male and female city dwellers, particularly in districts with higher psychiatric incidence and fewer green resources. Higher green-related affective benefit was related to lower prefrontal activity during negative-emotion processing, which suggests that urban green space exposure may compensate for reduced neural regulatory capacity.

Correction: Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.

[This corrects the article DOI: 10.1371/journal.pone.0162360.].

A System for Generating Customized Pleasant Pedestrian Routes Based on OpenStreetMap Data.

In this work, we present a system that generates customized pedestrian routes entirely based on data from OpenStreetMap (OSM). The system enables users to define to what extent they would like the route to have green areas (e.g., parks, squares, trees), social places (e.g., cafes, restaurants, shops) and quieter streets (i.e., with less road traffic). We present how the greenness, sociability, and quietness factors are defined and extracted from OSM as well as how they are integrated into a routing cost function. We intrinsically evaluate customized routes from one-thousand trips, i.e., origin⁻destination pairs, and observe that these are, in general, as we intended-slightly longer but significantly more social, greener, and quieter than the respective shortest routes. Based on a survey taken by 156 individuals, we also evaluate the system's usefulness, usability, controlability, and transparency. The majority of the survey participants agree that the system is useful and easy to use and that it gives them the feeling of being in control regarding the extraction of routes in accordance with their greenness, sociability, and quietness preferences. The survey also provides valuable insights into users requirements and wishes regarding a tool for interactively generating customized pedestrian routes.

A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information.

The growing use of crowdsourced geographic information (CGI) has prompted the employment of several methods for assessing information quality, which are aimed at addressing concerns on the lack of quality of the information provided by non-experts. In this work, we propose a taxonomy of methods for assessing the quality of CGI when no reference data are available, which is likely to be the most common situation in practice. Our taxonomy includes 11 quality assessment methods that were identified by means of a systematic literature review. These methods are described in detail, including their main characteristics and limitations. This taxonomy not only provides a systematic and comprehensive account of the existing set of methods for CGI quality assessment, but also enables researchers working on the quality of CGI in various sources (e.g., social media, crowd sensing, collaborative mapping) to learn from each other, thus opening up avenues for future work that combines and extends existing methods into new application areas and domains.

Mood Dimensions Show Distinct Within-Subject Associations With Non-exercise Activity in Adolescents: An Ambulatory Assessment Study.

Physical activity is known to preserve both physical and mental health. However, the physical activity levels of a large proportion of adolescents are insufficient. This is critical, since physical activity levels in youth have been shown to translate into adulthood. Whereas in adult populations, mood has been supposed to be one important psychological factor that drives physical activity in everyday life, this issue has been poorly studied in adolescent populations. Ambulatory Assessment is the state-of-the-art approach to investigate how mood and non-exercise activity fluctuate within persons in everyday life. Through assessments in real time and real life, this method provides ecological validity, bypassing several limitations of traditional assessment methods (e.g., recall biases). To investigate whether mood is associated with non-exercise activity in adolescents, we equipped a community-based sample comprising 113 participants, aged 12-17 years, with GPS-triggered e-diaries querying for valence, energetic arousal, and calmness, and with accelerometers continuously measuring physical activity in their everyday lives for 1 week. We excluded all acceleration data due to participants' exercise activities and thereafter we parameterized non-exercise activity as the mean value across 10-min intervals of movement acceleration intensity following each e-diary prompt. We used multilevel analyses to compute the effects of the mood dimensions on non-exercise activity within 10-min intervals directly following each e-diary prompt. Additionally, we conducted explorative analyses of the time course of the effects, i.e., on different timeframes of non-exercise activity up to 300 min following the mood assessment. The results showed that valence (p < 0.001) and energetic arousal (p < 0.001) were positively associated with non-exercise activity within the 10 min interval, whereas calmness (p < 0.001) was negatively associated with non-exercise activity. Specifically, adolescents who felt more content, full of energy, or less calm were more physically active in subsequent timeframes. Overall, our results demonstrate significant associations of mood with non-exercise activity in younger ages and converge with the previously observed association between mood and physical activity in adults. This knowledge on distinct associations of mood-dimensions with non-exercise activity may help to foster physical activity levels in adolescents.

Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques.

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.

Exercise versus Nonexercise Activity: E-diaries Unravel Distinct Effects on Mood.

INTRODUCTION: The association between physical activity and mood is of major importance to increase physical activity as a prevention strategy for noncommunicable diseases and to improve mental health. Unfortunately, existing studies examining how physical activity and mood wax and wane within persons over time in everyday life do show ambiguous findings. Taking a closer look at these studies reveals that the aggregation levels differ tremendously. Whereas mood is conceptualized as a three-dimensional construct, physical activity is treated as a global construct not taking into account its distinct components like exercise (such as jogging) and nonexercise activity (NEA; such as climbing stairs). METHODS: To overcome these limitations, we conducted an ambulatory assessment study on the everyday life of 106 adults over 7 d continuously measuring NEA via accelerometers and repeatedly querying for mood in real time via GPS-triggered e-diaries. We used multilevel modeling to derive differential within-subject effects of exercise versus NEA on mood and to conduct analyses on the temporal course of effects. RESULTS: Analyses revealed that exercise increased valence (beta = 0.023; P < 0.05) and calmness (beta = 0.022; P < 0.05). A tendency of decreasing energetic arousal (beta = -0.029) lacked significance. NEA, parameterized as 15-min episodes of physical activity intensity in everyday life, increased energetic arousal (beta = 0.135; P < 0.001) and decreased calmness (stand. beta = -0.080; P < 0.001). A tendency of increasing valence (beta = 0.014) lacked significance. Using longer time intervals for NEA revealed similar findings, thus confirming our findings. CONCLUSION: Exercise and NEA differed regarding their within-subject effects on mood, whereas exercise increased valence and calmness, NEA increased energetic arousal and decreased calmness. Therefore, it appears necessary to clearly differentiate between exercise and NEA regarding their within-subject effects on mood dimensions in both research and treatment.

A comparison of temporal and location-based sampling strategies for global positioning system-triggered electronic diaries.

Self-reporting is a well-established approach within the medical and psychological sciences. In order to avoid recall bias, i.e. past events being remembered inaccurately, the reports can be filled out on a smartphone in real-time and in the natural environment. This is often referred to as ambulatory assessment and the reports are usually triggered at regular time intervals. With this sampling scheme, however, rare events (e.g. a visit to a park or recreation area) are likely to be missed. When addressing the correlation between mood and the environment, it may therefore be beneficial to include participant locations within the ambulatory assessment sampling scheme. Based on the geographical coordinates, the database query system then decides if a self-report should be triggered or not. We simulated four different ambulatory assessment sampling schemes based on movement data (coordinates by minute) from 143 voluntary participants tracked for seven consecutive days. Two location-based sampling schemes incorporating the environmental characteristics (land use and population density) at each participant's location were introduced and compared to a time-based sampling scheme triggering a report on the hour as well as to a sampling scheme incorporating physical activity. We show that location-based sampling schemes trigger a report less often, but we obtain more unique trigger positions and a greater spatial spread in comparison to sampling strategies based on time and distance. Additionally, the location-based methods trigger significantly more often at rarely visited types of land use and less often outside the study region where no underlying environmental data are available.