Breakthrough changes in the rental market have occurred with the introduction of peer-to-peer accommodation services such as Airbnb. This phenomenon is attracting tourists who contribute to the sustainability of local trade and the economic development of the city. This research enriches the current debate on the range of factors that influence Airbnb accommodation prices. To that end, a method was developed to understand the relationship between Airbnb accommodation attributes and listing prices; and to consider variables related to the properties’ location and surrounding urban environment, considering the touristic characteristics of the four Spanish Mediterranean Arc cities selected as case study. A multivariable analysis technique is used for estimating a hedonic price model that adopts the ordinary least squares and the quantile regression methods. The findings obtained for the impact of location on listing prices are contrary to previous studies. In fact, accommodation prices increase incrementally by 1.3% per kilometer from the tourist area, which in all four cases are situated in the historic area of the city. However, at the same time, accommodation prices decrease incrementally as distance from the coastline increases. Lastly, results related to how the listings’ accommodation, host, and advertising characteristics impact Airbnb prices concur with previous studies.

Locative social media networks as open sources of data allow researchers and professionals to acknowledge which city places are preferred, used and livable. Following this hypothesis, this paper proposes a methodology to identify successful public spaces – plazas – through the location-based social media network Foursquare and to analyze their urban position using morphological and historical cartographies. The overall methodology comprises three stages. First, the most important cities of the province of Alicante were selected. Second, the most relevant plaza of each city was identified using data retrieved from the social network Foursquare. Finally, the location of each plaza is analyzed in relation to the historic center and the main axes of the city. Possible correlations between their urban location and their vibrant character were subsequently identified. Two findings have emerged from this study: (a) a strong spatial relationship exists between the most successful plazas and the historic city center, which reinforces their traditional social character; and (b) all plazas share two similar traits, their location within the urban network and their proximity to the main axes of the city.This work was supported by the University of Alicante. Project: Representation and interpretation of urban dynamics through LBSN data. MAPPINGAME. No. GRE15-14 2015

An insight on urban tourism-related phenomena is provided in this study by analysing open and volunteered user generated content. A reference framework method is proposed and applied to an illustrative case study to meet a twofold objective: to identify Tourist Activity Centre – TAC – areas based on their functional character – sightseeing, shopping, eating and nightlife; and, to obtain an up-to-date fine-grain characterization of the most dynamic zones in an urban context. Instasights Heatmaps and data from Location Based Social Networks – Foursquare, Google Places, Twitter and Airbnb – were used to depict tourist urban activity. This reproducible method transcends Instasights generic visualization of popular areas by exploiting the benefits of overlapping LBSN data sources. This method facilitates a granular analysis of tourism-related places of interest and makes headway in bridging the gap between traditional approaches and user preferences, revealed through digital footprints, for urban analysis. The results indicate the potential of this method as a complementary tool for urban planning decision-making.

This study revisits the debate surrounding the definition of neighborhood boundaries by addressing the disconnect between the city’s Administrative Neighborhoods and its functional organization. A method is proposed for dividing the city into more meaningful units through the spatial distribution of urban activities by retrieving data from Google Places. The dataset was pre-processed and spatially divided into Functional Clusters. A comparison between functional and administrative subdivisions of the city was undertaken, from which three overall conclusions could be drawn. First, a function-based city partition allows economically active urban areas to become the neighborhood’s center, thereby creating a polynuclear neighborhood structure that would potentially encourage greater cross-movement of people throughout the city. Second, the specialization of activities becomes more evident in Functional Clusters than in Administrative Neighborhoods. Third, access to up-to-date data makes possible a timely diagnosis of the quantity and diversity of urban activities—i.e., economic activities, services, and facilities—through Google Places data. The value of this contribution is to inform urban decision-making and policies in order to better balance the provision of a neighborhood’s economic activity.