English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Understanding the dynamics by which urban areas attract visitors issignificant for urban development in cities. In addition, identifying servicesthat relate to highly attractive districts is useful to make policies regardingthe placement of such places. Thus, we present a framework for classifyingdistricts in cities by their attractiveness to visitors, and relating Points ofInterests (POIs) types to districts' attraction patterns. We usedOrigin-Destination matrices (ODs) mined from cell phone data that capture theflow of trips between each pair of places in Riyadh, Saudi Arabia. We definethe attraction profile for a place based on three main statistical features:The amount of visitors a place received, the distribution of distance traveledby visitors on the road network, and the spatial spread of where visitors comefrom. We use a hierarchical clustering algorithm to classify all places in thecity by their features of attraction. We detect three types of Urban Attractorsin Riyadh during the morning period: Global which are significant places in thecity, Downtown which the central business district and Residential attractors.In addition, we uncover what makes these places different in terms ofattraction patterns. We used a statistical significance testing approach torigorously quantify the relationship between Points of Interests (POIs) types(services) and the 3 patterns of Urban Attractors we detected. The proposedframework can be used for detecting the attraction patterns given by type ofservices related to each pattern. This is a critical piece of information toinform trip distribution models.

Urban Attractors: Discovering Patterns in Regions of Attraction in Cities