Active commuting provides routine-based regular physical activity which can reduce the risk of chronic diseases. Using public transportation involves some walking or cycling to a transit stop, transfers and a walk to the end location and users of public transportation have been found to accumulate more moderate physical activity than non-users. Understanding how public transportation characteristics are associated with active transportation is thus important from a public health perspective. This study examines the associations between objective measures of access to public transportation and self-reported active commuting. Self-reported time spent either walking or cycling commuting each day and the distance to workplace were obtained for adults aged 16 to 65 in the Danish National Health Survey 2010 (n = 28,928). Access to public transportation measures were computed by combining GIS-based road network distances from home address to public transit stops an integrating their service level. Multilevel logistic regression was used to examine the association between access to public transportation measures and active commuting. Distance to bus stop, density of bus stops, and number of transport modes were all positively associated with being an active commuter and with meeting recommendations of physical activity. No significant association was found between bus services at the nearest stop and active commuting. The results highlight the importance of including detailed measurements of access to public transit in order to identify the characteristics that facilitate the use of public transportation and active commuting.

The increased availability of transit schedules from web sites or travel planners as well as more disaggregate data has led to a growing interest in creating individual public transportation accessibility measures. However, used extensively, standard GIS software does not have direct capabilities to integrate transit schedules into multimodal networks and measure space–time-based accessibility. This has caused authors to either simplify travel time elements or develop tools to overcome these challenges. In this paper we aim to describe and implement a method that enables integrating time-table data from a travel planner into a multimodal network model using simple SQL (structured query language) programming and standard GIS. The method presented here integrates all parts of travelling by public transportation from individual home addresses to all reachable transit stops within different travel time thresholds. The method is used successfully to create a multimodal travel-time network model of the Capital Region of Denmark comprising bus, train, light rail, metro, and ferry as well as integrating walking or cycling to stops. Here, the individual accessibility is defined as accessibility areas. The accessibility areas are created at morning rush hour for a study population of 29 447 individuals and a few examples of accessibility areas are presented. The results show a big difference in individual public transportation accessibility in the region. In addition, how the transit network is accessed, whether it is at the nearest stop or at all stops within 1 km walking distance or 3 km cycling distance, leads to very different accessibility areas.

Land-use modelling and spatial scenarios have gained attention as a means to meet the challenge of reducing uncertainty in spatial planning and decision making. Many of the recent modelling efforts incorporate cellular automata to accomplish spatially explicit land-use-change modelling. Spatial interaction between neighbouring land uses is an important component in urban cellular automata. Nevertheless, this component is often calibrated through trial-and-error estimation. The aim of this project has been to develop an empirically derived landscape metric supporting cellular-automata-based land-use modelling. Through access to very detailed urban land-use data it has been possible to derive neighbourhood rules empirically, and test their sensitivity to the land-use classification applied, the regional variability of the rules, and their time variance. The developed methodology can be implemented easily and thus used as a much needed replacement for the various trial-and-error approaches that are often applied in land-use modelling.

Knowledge of ecosystem services (ES) and the benefits provided by offshore marine areas, including the welfare impacts from the establishment of marine protected areas (MPAs) is still limited. In the present study we evaluated benefits from ES, citizens’ willingness-to-pay for potential changes in the provision of ES, and welfare losses to citizens due to restrictions on economic activities from establishing new offshore MPAs in Latvian waters. The scenarios for the economic valuation were based on analysing the supply of ES from the protected marine habitats, showing changes in the ES supply in policy relevant scenarios of the MPA size. Our study evaluates a wide array of ES delivered by offshore protected habitats and reveals that citizens’ willingness-to-pay for preserving habitats and ES supply exceeds their welfare losses from restrictions in economic activities. Our approach supports the prioritisation of habitat types according to their contribution to ES supply and benefits for citizens. The analysis can be complemented with spatial data regarding distribution of habitats, providing an opportunity to identify areas with the highest ES benefits to support marine protection and spatial planning.