Cultural Ecosystem Service (CES) evaluation is the basis for various environmental management and policymaking for nature-based tourism destinations including national parks. As an innovative data sourcing method, geolocated social media data promotes the rapid growth in CES research. Based on 58 empirical research articles using geolocated social media data, we review the current status and challenges of CES research and suggest some future CES research directions. We find that current geolocated social media based quantitative CES evaluation studies concentrate in photo density analysis, quantitative analysis of photo contents, photo viewshed analysis and economic values analysis. We also find that factors affecting CES spatial and temporal distributions fall in two major categories: biophysical and social/infrastructure. We think that future CES research can be improved by theoretical extension, methodological integration and service co-creation.

A transit network is a spatiotemporally dynamic system whose topology, components, and services all change over time. The complexity and dynamic nature of transit networks make it difficult to model by traditional transportation models. This paper presents an object‐oriented data model that coherently represents space, time, and dynamics of transit networks and supports schedule‐based pathfinding. In this data model, a transit network and its components are modelled as spatiotemporal objects that have properties and behaviours. Unlike traditional roadway transportation network models that define topology as connectivity between geometric elements such as edges and nodes, this data model uniquely defines topology as sequences and collections of transit features which are application domain objects. Moreover, this data model supports the generation of active network topologies based on given times to represent real‐time network states as well as to reduce topological redundancy. Implementations of the data model in two Internet transit trip planners have demonstrated that it can effectively reduce computation complexity of schedule‐based pathfinding algorithms and enhance pathfinding performance.

To measure job accessibility, person-based approaches have the advantage to capture all accessibility components: land use, transportation system, individual’s mobility and travel preference, as well as individual’s space and time constraints. This makes person-based approaches more favorable than traditional aggregated approaches in recent years. However, person-based accessibility measures require detailed individual trip data which are very difficult and expensive to acquire, especially at large scales. In addition, traveling by public transportation is a highly time sensitive activity, which can hardly be handled by traditional accessibility measures. This paper presents an agent-based model for simulating individual work trips in hoping to provide an alternative or supplementary solution to person-based accessibility study. In the model, population is simulated as three levels of agents: census tracts, households, and individual workers. And job opportunities (businesses) are simulated as employer agents. Census tract agents have the ability to generate household and worker agents based on their demographic profiles and a road network. Worker agents are the most active agents that can search jobs and find the best paths for commuting. Employer agents can estimate the number of transit-dependent employees, hire workers, and update vacancies. A case study is conducted in the Milwaukee metropolitan area in Wisconsin. Several person-based accessibility measures are computed based on simulated trips, which disclose low accessibility inner city neighborhoods well covered by a transit network.