This study will compare the results of measuring Urban Complexity using the Shannon-Wiener index in two different methods. Using a joint dataset retrieved from Foursquare API, we will measure the degree of urban complexity of every street: 1) relating every amenity to the closest street segment in a computational way and then applying the calculation to the segments; and 2) applying the calculation to every cell of a grid that will be combined with the street network afterwards. The selected case study is the city of London, and the dataset employed will be retrieved from Foursquare. Over 79,000 venues were collected and classified in over 660 categories. In order to proceed to the analysis, these 660 categories will be reduced to 10 based on the classification of activities observed in the public space from the traditional urban discipline. Then the urban complexity index of each street segment of London will be measured as a simultaneous calculation of the density and diversity of collected and classified economic activities.

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 facilitiesthrough 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.

Este trabajo propone un acercamiento al estudio de la producción de identidad y sentimiento de apropiación espacial en determinados entornos urbanos. Identidad y apropiación son estudiados desde el análisis de hitos y topónimos, utilizando para ello big data desde Foursquare, red social en la cual los usuarios publican sus visitas a lugares. Se analizan varios crecimientos urbanos de la reciente burbuja inmobiliaria en el Área Metropolitana de Madrid (1990-2012), sospechosos de falta de identidad por la reciente y rápida urbanización, así como por posibles mecanismos de simplificación del tejido urbano. El análisis valora diferentes tipos de crecimiento, revelando una correspondencia entre la tipología morfológica de los desarrollos y el tipo de lugares que constituyen hitos en ellas, así como con la aparición de topónimos, destacando ciertos lugares como elementos potenciales generadores de identidad social urbana.

Pedestrian activity is a cornerstone for urban sustainability, with key implications for the environment, public health, social cohesion, and the local economy. Therefore, city planners, urban designers, and decision-makers require tools to predict pedestrian mobility and assess the walkability of existing or planned urban environments. For this purpose, diverse approaches have been used to analyze different inputs such as the street network configuration, density, land use mix, and the location of certain amenities. This paper focuses on the location of urban amenities as key elements for pedestrian flow prediction, and, therefore, for the success of public spaces in terms of the social life of city neighborhoods. Using agent-based modeling (ABM) and land use floor space data, this study builds a pedestrian flow model, which is applied to both existing and planned areas in the inner city of Hamburg, Germany. The pedestrian flows predicted in the planned area inform the ongoing design and planning process. The flows simulated in the existing area are compared against real-world pedestrian activity data for external validation to report the model accuracy. The results show that pedestrian flow intensity correlates to the density and diversity of amenities, among other KPIs. These correlations validate our approach and also quantify it with measurable indicators.

El análisis del espacio público urbano a partir de su dimensión perceptual ha sido, tradicionalmente, un recurso fundamental para reconocer qué características físicas lo diferencian y le confieren actividad y vitalidad. Esta aproximación es particularmente relevante en entornos urbanos transformados. Conocer la percepción social  sobre el espacio público permite valorar en qué medida las acciones realizadas modifican las dinámicas urbanas. Bajo esta consideración, el objetivo de este trabajo es estudiar la vida urbana tras la transformación de la Calle San Francisco, en Alicante, España,  en un espacio lúdico caracterizado por la implantación de una serie de setas gigantes. Para ello, se definieron y evaluaron tres indicadores de vida pública: las actividades económicas en la edificación antes y después de la intervención; el uso social del espacio proyectado; y, la imagen pública —o imagen percibida— resultante. Se propone una metodología híbrida que combina los datos recogidos en un estudio de campo con los datos provenientes de fuentes de base tecnológica, concretamente, de la red social Instagram y del servicio web Google Street View. Los resultados obtenidos ponen de manifiesto que la utilización de datos offline y online permite confirmar, complementar o cuestionar las deducciones parciales de cada uno de los métodos, ratificando así que la transformación de la Calle San Francisco ha modificado sustancialmente no sólo su identidad física, sino sobre todo la imagen colectiva del espacio con relación a la del resto de la ciudad. En definitiva, este trabajo evidencia la pertinencia de incorporar técnicas de base tecnológica para entender la complejidad de las relaciones sociales, físicas y virtuales, que se producen como consecuencia de la transformación de un espacio urbano.