Yerevan is the capital and largest city of Armenia and one of the world’s oldest continuously inhabited cities. Situated along the Hrazdan River, Yerevan is the administrative, cultural, and industrial center of the country. It has been the capital since 1918, the fourteenth in the history of Armenia.
Yerevan has 12 administrative districts covering an area of 223km2, a population over a million inhabitants and an average density of 4,824/km2.
SPIN unit was commissioned by UNDP in cooperation with Strelka KB to perform several spatial analytics of Yerevan using mainly social media and activity data.
Using data to analyze urban space
Users’ performativity in crowd-sourced locative platforms and especially location-based social networks centred on urban spaces is contingent on the places where the actions take place. The great potential of crowd-sourced data is being exploited to analyze a diverse range of topics related to the functional organization of the city (Arribas‐Bel and Tranos, 2018), such as: the relationship between urban form and function (Crooks et al., 2015, 2016); the identification of POIs —points of interest— (Van Canneyt et al., 2012; Deng and Newsam, 2017; García-Palomares et al., 2015; Van Weerdenburg et al., 2019) and their accessibility in terms of density and diversity (Shen and Karimi, 2016); the characterization of Livehoods according to the collective behaviors of residents (Cranshaw et al., 2012); and, the delimitation of functional areas to understand social and spatiotemporal aspects of the city (Chen et al., 2017; Rösler and Liebig, 2013).
Structure, function and form are not sufficient for the generation of social relations; they can only favour it. In the context where social platform becomes ubiquitous, the socio-cultural expression and economic transactions are no longer space-bound. Instead they dramatically influence the determination of social needs, which can be influenced by global trends as well as by local phenomena.
In this context, the collection and study of activities that people engage with becomes fundamental to learn more about contemporary life in the city. In urban planning, there is a long-lasting tradition of functional zoning and, more generally, a consideration towards classifying urban structures and infrastructures according to functional typologies – which separate for example housing, leisure and work, as well as organisational typologies that distinguish public and private spaces and providers. If on the one hand the functional classification of amenities has its values in regards to planning and regional law, it does not consider the actual use that dwellers make of urban structures (Cerrone, López Baeza and Lehtovuori, 2020).
Optional vs Necessary
The presence of Optional and/or Necessary places have different consequence for the liveliness of urban space. Here we represent the number of existing places of each of the two groups, for every district.
The central district stands out for a high concentration of both, however the proportion between Optional and Necessary shifts towards Optional in relation to the other districts, as it happens to Nor Nork and Malatya Sebastya in a lower degree.
Assiduity
Number of different users (Y), Number of visits (X) calculates how many times, on average, the same people visit the same places.
High number of users and low number of visits in a place represents a reduced number of people visiting the place on a regular repeated basis, therefore it would indicate that the place is close to routinary activities.
On the contrary, a higher number of different users and would mean that the place is not only visited always by the same group of people, but by a wider variety of visitors pointing towards leisure activities and exceptional (not everyday).
Overall, the impact of a place for the surrounding urban fabric usually correlates to the intensity of both indicators as portrayed in the graph, the distance to the origin (0, 0)
Centrality and accessibility
The centrality of streets, based on Space Syntax analysis of the street network, calculates the likelihood of a street segment to host pedestrian movement.
Central streets are better connected to more places in the city. The index of centrality indicates how well connected a street is, considering all other streets in the city.
The route marked in blue shows correspondence between centrality and activity and popularity levels: Commercial axes with high centrality.
Accessible areas – Areas with high accessibility are located closer to main access roads and highways, and the city center. Accessibility is computed as in how easy is to reach all other places in the city from them. They tend to be the origin and destination of most trips of daily basis, generally (1) commuting from home to work, (2) logistic transport, (3) daily recreational and leisure activities.
Accessible intersections – Intersections with high accessibility are located in the crossing of two or more main roads (highways). From those intersections, the majority of places in the city can be reached. In those intersections traffic tends to be higher. Due to high traffic and large infrastructural requirements, they tend to be isolated.
Activity in the city
Mapping activities begins with retrieving the Foursquare dataset of all urban amenities and categorising them to the nature of the activity that can be performed in them. Traditionally, Jan Gehl deployed a reliable classification system to study public space and the links between design and public life. Even though Gehl’s focus is in small public spaces such as streets, squares and parks, his approach can be used to survey urban space in general thanks to its elemental yet powerful categorization. The applicability of a classification to all kinds of activity within the city should be regardless of space characteristics – including also those performed in indoor or private spaces. Operationalising and scaling up this approach would potentially allow estimating and study activity patterns for one or many cities while keeping the resolution of the analysis at a human scale. The core benefit of this approach including crowd-sourced data is the possibility to chart tangible environment with the elements that are intangible but valuable for the social and economic success of one neighbourhood and its streets and squares (Cerrone, López Baeza & Lehtovuori, 2020).
