Consumption and symbolic capital in the metropolitan space: Integrating ‘old’ retail data sources with social big data

Beyond land use mix, walkable trips. An approach based on parcel-level land use data and network analysis

“…However, we have chosen two different walkable cutoff distances to capture reasonable, relevant limits for human walking: 600 metres (as a standard proxy to a 10-minute walk) and 1200 metres (as a proxy to a 20-minute walk). The literature has considered the 600-metre distance (or 1/3 mile) as a reasonable representative walking distance (Carpio-Pinedo & Gutiérrez, 2020;Sevtsuk, 2014;2003) since it captures most of pedestrian activity according to empirical studies (Handy & Niemeier, 1997;Pushkarev & Zupan, 1975). The second threshold (1200 m) is a better proxy to consider people with reduced mobility, tests the sensitivity of the approach and the degree of land use imbalance overcome by increasing the walkable distance, and captures the idea that urban spatial network configuration might have different (yet complementary) functioning at local and global scales (an approach to our knowledge initiated Hillier et al, 1986).…”

“…Further, future research could explore different ways to weight destinations beyond their floor area, e.g. considering the popularity and symbolic dimension of places (Carpio-Pinedo & Gutiérrez, 2020). Further research must also evaluate the possibility of using different walking distances for different land-use types, other types of accessibility measures (e.g.…”

The Geography of Social Media Data in Urban Areas: Representativeness and Complementarity

“…Other lines of research addressed LBSN data generation and the social differences or inequalities in certain geographic contexts [4,37,38]. For example, in the case conducted in Madrid, Spain, the majority of hotspots registered on Foursquare were located in the northern, wealthier half of the metropolitan area, characterized by low density and housing sprawl that has an important sociodemographic contrast with the poorer southern areas, with lower income levels, college education attainment and low employment rates [4].…”

“…Other lines of research addressed LBSN data generation and the social differences or inequalities in certain geographic contexts [4,37,38]. For example, in the case conducted in Madrid, Spain, the majority of hotspots registered on Foursquare were located in the northern, wealthier half of the metropolitan area, characterized by low density and housing sprawl that has an important sociodemographic contrast with the poorer southern areas, with lower income levels, college education attainment and low employment rates [4]. A different case was the analysis of geotagged social media such as Twitter in the city of Louisville, US, which evidenced that despite the inequalities among different areas of the city where racial segregation was highly present, the geographical extent of these areas was not necessarily evidenced by the socio-spatial behavior of the population.…”

“…A great deal of scholarship in the field of LBSNs data applied to the study of urban phenomena focuses on large dense urban areas with important amounts of data [2,3]. These include metropolitan areas with a high population density [4]; areas where urban activities are more likely to happen, such as commercial areas; or areas where points of interest are concentrated, as opposed to predominantly residential areas [5,6] where the amount of available data may not be considered sufficiently representative to draw meaningful conclusions. Indeed, analyzing LBSN data requires strategies to keep the size of the datasets manageable, whilst there must be sufficient gathered data to obtain rigorous findings [7].…”