María Henar Salas-Olmedo

The eruption of Airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona

In recent years, what has become known as collaborative consumption has undergone rapid expansion through peer-to-peer (P2P) platforms. In the field of tourism, a particularly notable example is that of Airbnb. This article analyses the spatial patterns of Airbnb in Barcelona and compares them with hotels and sightseeing spots. New sources of data, such as Airbnb listings and geolocated photographs are used. Analysis of bivariate spatial autocorrelation reveals a close spatial relationship between Airbnb and hotels, with a marked centre-periphery pattern, although Airbnb predominates around the citys main hotel axis and hotels predominate in some peripheral areas of the city. Another interesting finding is that Airbnb capitalises more on the advantages of proximity to the citys main tourist attractions than does the hotel sector. Multiple regression analysis shows that the factors explaining location are also different for hotels and Airbnb. Finally, it was possible to detect those parts of the city that have seen the greatest increase in pressure from tourism related to Airbnbs recent expansion.

Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

Accessibility is essentially a dynamic concept. However, most studies on urban accessibility take a static approach, overlooking the fact that accessibility conditions change dramatically throughout the day. Due to their high spatial and temporal resolution, the new data sources (Big Data) offer new possibilities for the study of accessibility. The aim of this paper is to analyse urban accessibility considering its two components –the performance of the transport network and the attractiveness of the destinations– using a dynamic approach using data from TomTom and Twitter respectively. This allows us to obtain profiles that highlight the daily variations in accessibility in the city of Madrid, and identify the influence of congestion and the changes in location of the population. These profiles reveal significant variations according to transport zones. Each transport zone has its own accessibility profile, and thus its own specific problems, which require solutions that are also specific.

Immigrant community integration in world cities

As a consequence of the accelerated globalization process, today major cities all over the world are characterized by an increasing multiculturalism. The integration of immigrant communities may be affected by social polarization and spatial segregation. How are these dynamics evolving over time? To what extent the different policies launched to tackle these problems are working? These are critical questions traditionally addressed by studies based on surveys and census data. Such sources are safe to avoid spurious biases, but the data collection becomes an intensive and rather expensive work. Here, we conduct a comprehensive study on immigrant integration in 53 world cities by introducing an innovative approach: an analysis of the spatio-temporal communication patterns of immigrant and local communities based on language detection in Twitter and on novel metrics of spatial integration. We quantify the Power of Integration of cities –their capacity to spatially integrate diverse cultures– and characterize the relations between different cultures when acting as hosts or immigrants.

The use of public spaces in a medium-sized city: from Twitter data to mobility patterns

This research evidences the usefulness of open big data to map mobility patterns in a medium-sized city. Motivated by the novel analysis that big data allow worldwide and in large metropolitan areas, we developed a methodology aiming to complement origin-destination surveys with à la carte spatial boundaries and updated data at a minimum cost. This paper validates the use of Twitter data to map the impact of public spaces on the different parts of the metropolitan area of Concepción, Chile. Results have been validated by local experts and evidence the main mobility patterns towards spaces of social interaction like malls, leisure areas, parks and so on. The map represents the mobility patterns from census districts to different categories of public spaces with schematic lines at the metropolitan scale and it is centred in the city of Concepción (Chile) and its surroundings (~10 kilometres).ABSTRACT This research evidences the usefulness of open big data to map mobility patterns in a medium-sized city. Motivated by the novel analysis that big data allow worldwide and in large metropolitan areas, we developed a methodology aiming to complement origin-destination surveys with à la carte spatial boundaries and updated data at a minimum cost. This paper validates the use of Twitter data to map the impact of public spaces on the different parts of the metropolitan area of Concepción (MAC), Chile. Results have been validated by local experts and evidence the main mobility patterns towards spaces of social interaction like malls, leisure areas, parks and so on. The Main Map represents the mobility patterns from census districts to different categories of public spaces with schematic lines at the metropolitan scale and it is centred in the city of Concepción (Chile) and its surroundings (∼10 kilometres).

Assessing accessibility with local coefficients for the LUTI model MARS

Abstract Accessibility-based land use and transport interaction (LUTI) models are tools for policy assessments that facilitate coherent implementation of sustainable strategic urban plans. This study aims to improve one of those LUTI models introducing the different impact of factors influencing residential and workplace choice by computing local coefficients. In particular, this research explores the methodology of integrating the public choice model into the MARS (Metropolitan Activity Relocation Simulator) model using a complex accessibility indicator. We established a new approach to input the variation of the influence of each public service across space with the use of Geographically Weighted Regression (GWR). The model update and extension of MARS are all based on the Region of Madrid, Spain. Using the new accessibility indicator yielded better results and corrected some under estimation and overestimations in the number of workplaces. The correlation using the new accessibility indicator is significantly higher than the one using the old one. The prediction using the new indicator achieves better results for the whole area whatever the zone is small or large. The analysis evidences the convenience of GIS and LUTI combination to improve model accuracy and precision. Using the new accessibility indicator based on local coefficients, MARS model fits better with the real data in respect of the distribution of workplaces and residents, which are the key representatives of the land use sub-model.

People- and Place-Sensitive Perspective for Studying the Potential for the Role of Jobcentres

The aim of this paper is to analyze how the geospatial distributions of unemployed people and jobcentres affects the probability of finding a job. We estimated a latent class logistic regression to analyze the probability of a transition to employment while controlling for individual and location heterogeneity. We compute the median value of the odds ratios between the area with the highest employment probability and the area with the lowest employment probability when randomly selecting areas. The results support the need to develop employment policies that take into account a place-based environment and to investigate how an institution’s impact can be people- and place-sensitive. An additional objective of this paper is to discuss this issue and suggest the need for the formulation of targets at jobcentres as well as the division of funding according to the targets and results that are to receive welfare advantages