The impact of interventions in the built environment on physical activity levels: a systematic umbrella review.

Physical activity is good for people's health. The relationship between the built environment and physical activity has been well documented. However, evidence is both scarce and scattered on specific urban interventions, i.e., intentional redesigns of the built environment that promote physical activity accompanied by pre- and post-effect measurement. This umbrella review aims to synthesize the findings of systematic reviews focused on these urban interventions. We followed the PRISMA 2020 and JBI umbrella review protocol guidelines and searched seven databases covering the period between Jan 2010 and April 2022 using keywords relating to the built environment, health, physical activity, and interventions. This yielded seven systematic reviews, in which we identified several urban interventions that can promote physical activity. We found positive effects of urban interventions on physical activity regarding park renovations, adding exercise equipment, introducing a (new) pocket park, improving cycling environments, improving walking & cycling environments, as well as multi-component initiatives for active travel and enhancing the availability & accessibility of destinations. The findings suggest that the urban environment can effectively promote physical activity, especially by adding various facilities and destinations and by making the environment better suitable for active use.

Sensing Human Activity: GPS Tracking.

THE ENHANCEMENT OF GPS TECHNOLOGY ENABLES THE USE OF GPS DEVICES NOT ONLY AS NAVIGATION AND ORIENTATION TOOLS, BUT ALSO AS INSTRUMENTS USED TO CAPTURE TRAVELLED ROUTES: as sensors that measure activity on a city scale or the regional scale. TU Delft developed a process and database architecture for collecting data on pedestrian movement in three European city centres, Norwich, Rouen and Koblenz, and in another experiment for collecting activity data of 13 families in Almere (The Netherlands) for one week. The question posed in this paper is: what is the value of GPS as 'sensor technology' measuring activities of people? The conclusion is that GPS offers a widely useable instrument to collect invaluable spatial-temporal data on different scales and in different settings adding new layers of knowledge to urban studies, but the use of GPS-technology and deployment of GPS-devices still offers significant challenges for future research.

Why GPS makes distances bigger than they are.

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is - on average - bigger than the true distance between these points. This systematic 'overestimation of distance' becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.