Archive for June, 2019

An exploration on the social impact that access to the ressources of the city and the presence of economical, cultural, political systems generate in the urban landscape.

Team members: Andrés Gómez Mares, Pablo Goldin, Mikhail Pikman, Iktihab Ali External advisers: Sebastián Estremo


According to a study on marginalization realized by the Elliott School of International Affairs & the World Fair Trade Organization-Asia in 2015, the term can be defined as “ a condition and a process that prevents individuals and groups from full participation in social, economic, and political life enjoyed by the wider society.” This definitions that implies the existence of physical, political, social and economical limits and borders in the city that shape the life of it’s inhabitants also suggest that this limits are not static but are also constantly evolving by the correlation of this factors who add to each others creating a virtual “hotspot” map of this multifaceted cloud that somehow generates this “deterministic storm” on the city inhabitants.

This addition phenomenon and the limits it generates can also be explained by this second definition created by the Zentrum für Entwicklungsforschung Center for Development Research University of Bonn; “Marginality is an involuntary position and condition of an individual or group at the margins of social, political, economic, ecological and biophysical systems, preventing them from access to resources, assets, services, restraining freedom of choice, preventing the development of capabilities, and eventually causing extreme poverty. The poorest themselves have described their situation as being trapped in a “complex knot which can lead to further knots if the wrong threads are pulled.”

On the other hand, the concept of “density” in the urban field represents a delicate balance in the urban discussion where its presence is simultaneously perceived as a cause and also a solution for some of the problems that are described by marginalization.

Therefore, the analysis of the city of Moscow thru this two concepts would allow us to integrate qualitative and quantitative data into a single map who can express a complex social, spatial and temporary reality of the city and provide one more answer to a larger debate on the shaping of contemporary cities.

Research question

What is the relation between the concept of “density “and the overall idea of marginalization in the city of Moscow? How marginalization is related to the city of Moscow?

Hypothesis 1: The lower the density the higher the marginalization
Hypothesis 3: Populations in proximity to the City Center are less marginal than the ones in peripheries

Research method

In order to collect the necessary data for the analysis, we used secondary data and archival from open sources to have a strong quantitative base that later on was crossed with qualitative research elements and notions.

Crime in Moscow for example is a topic that has very few geolocalized information so we researched on different medias, news papers and social medias and we found this info graphic that gave us the enough information to start making correlations.

To collect information related to the concept of density, we started by working with the physical aspects of the city such as the area of the districts that was introduced in the map as a geometric shape in .geojason that we combined with the presence of different actors collected in open street map that could work as indicators of the dimensions that where going to be analyzed for the marginality map such as:

  • Schools = Educational
  • Police Stations = Governance
  • Hospitals = Health
  • Churches = Faith and tradition
  • Metro stations = Accessibility
  • Parks = Ecology
  • Market 24h = Wealth
  • Crime / 10 000 = Violence

All the collected data we merged into one CSV file that we used to make the different graphics in QGIS

By the definition of the dimensions parameters it will that allow us to generate a map that can only expose if a level of something is achieved according to some political, social, economical and physical parameters that by being overlapped would create an image that could express a complex concept such as marginality like in the example bellow.

source: Bonn University

Resultants and discussions


We decided to focus our research on the concept of the density of population by district and the presence of schools, churches and health infrastructure in these areas according to the data collected in Open street map and collected in Over pass turbo.

  1. Density of population

2. Presence of schools

3. Presence of churches

4. Presence of Health infrastructure

5. Presence of Police Stations


Using the excel table we generated a dashboard to show the correlations between different aspects in a dynamic table. In the example bellow we made a selection of the top 10 districts with more crimes committed for every 10 000 and we analyzed how the other categories are expressed in those 10 districts to have a correlation between the committed crimes a the presence of each of the commodities.

File without macros and with macros


In order to achieve our marginality map we created this specific dimensions that would generate the “positive and negative” layers of the heatmap according to some sensible indicators. Nevertheless, since all of these indicators are represented in identical geometries the map will show the “intensity” of the “marginality” by the number of overlappings of the defined dimensions creating a heatmap of it. All five dimensions have 20% opacity creating a scale of marginality in 5 steps.

QGIS instruments

In order to only show a certain amount of information from a table it is necessary to use the field calculator tool that allows us to select data from the attributes table according to specific instructions. In this case we used

// “fieldname” < value


  1. Health and distribution of public infrastructure: Density of hospitals for more than 10 000 persons < 0.35, the average of clinics per district for every 10 000 persons. This indicator helps us to measure the physical access to outpatient health care services and the position of the district compared to the others.
  2. Education: Density of schools for every 10 000 persons < 0.36, the average of schools per district for every 10 000 persons. This indicator helps us to understand the accessibility that citizens have to education in their own district and the impact that this facility can have in their daily life in terms of transportation for the involved persons but also in the independence that children can have from their parents if the distance between their houses and their schools allow them to walk or the impact on the work of the parents if they need to transport their children to school in another district and then go to their work.
  3. Security: Density of police stations for every 10 000 < 0,39, the average of police stations per district for every 10 000 persons. With this indicator we can challenge the perception that the number of crimes that occur for every 10 000 persons creates because some of the crimes might not be denounced in areas where relation with police is not common as much as the type of crime can variate between one district and another creating another layer of violence and repercussions that the number does not provide. The number of police stations therefore expose the resources that are invested in the security of a district compared to the others as much as the necessity of control in them.
  4. Faith and traditions: Density of churches for every 10 000 < 0,32, the average of churches per district for every 10 000 persons. This indicator help us to understand how the presence of the Church as an institution can be correlated to other primary aspects of life in urban environments such as health, education and security and also to understand its presence in the city.
  5. Medical commercial services: Density of pharmacies for every 10 000 < 3, the average of pharmacies facilities per district. While the number of hospitals per district express the access of the population to the health facilities provided by the government and financed on public budgets, the presence of pharmacies expose the commercial geographical strategy from the pharmaceutical sector to make profit from the ill populations. The discrepancy of presence of hospitals and pharmacies then expose the tensions between private or public strategies on infrastructure related to urban planning from hospitals and market based presence of pharmacies that can be related to the either to the proximity of hospitals, the health conditions of a populations that would be translated into consumption of medical products or the lack of hospitals that is replaced by pharmacies where persons satisfy their access to medical aid.

Conclusions and limitations

Hypothesis 1: The lower the density the major the marginalization
Observation: Some of the districts with low density are higly marginated nevertheless, the districts in the South center area of the city who have high density are also marginalized while other distircts in the west with low density are not marginalized under these parameters.
Hypothesis 2: Populations in proximity to the City Center are less marginal than the ones in peripheries.
Observation: The marginality map do not express such a clear contrast between “center and periphery” but more on cardinal references.

This kind of analysis create a dialectic that could be infinitely developed and correlated since every aspect of the city impacts on the other. Nevertheless, it is interesting for us to understand how an element can become and indicator by the position it occupies and the correlation of the information.

Moscow is a highly centralized city which could be perceived as a way of marginalization of its inhabitants depending on their proximity to the center of the city, nevertheless, the strong transportation system can be a factor that change the map of marginality as much as the quality and access to the public services described since proximity cannot be the only aspect that impacts on the experience of the users to such facilities. Other aspects which are not expressed in the graphics such as income could also transform the perception of the districts.

By further explorations and the additions of new information based not only in the shape of the districts but other geometries and other indicators the map would create a different image of the notion of marginality.

Team members: Alexei Smirnov | Valeria Cherekaeva | Renata Abdrafikova


McDonald’s is one of the most successful chains which offers fast food all around the world. To Russian audience, McDonald’s was introduced in 1990 and brought to Soviet people fizzy drinks, slim potato slices, and hamburgers. The popularity at that time can be explained with the novelty of the product. Today it might be explained with the habit or convenience of the service. The study by Romir (2018) showed that general attendance to the fast food restaurants increased at the end of 2018. The result of the survey showed that 72% of participants visited McDonald’s at least ones in three months, however last year the number was 67%. 

Due to the business of the citizens, food delivery companies come to the market with a service which provides to user’s opportunity to order food from a variety of restaurants, even if a restaurant does not offer delivery. Yandex.Eda is one of the most successful delivery services on the Russian market and their delivery guys can be seen everywhere in Moscow. Story of Yandex.Eda starts from 2016 when the company had a different name and different owner, in the shape as it is today company started to operate in 2018. Today Yandex.Eda in partnership with 9000 cafes and restaurants, however, Maxim Firsov, CEO Yandex Eda, mentioned that people order more often burgers, sushi, and pizza.

Research Question

In this study, we want to investigate if people order McDonald’s more often to the office or to the home. Potentially, this research can be used to understand the behavior of customers. Our hypothesis builds on the idea that office workers order McDonald’s due to the fast delivery and good customer services, however, people who order McDonald’s to home might like the taste of the product. Also, both groups might be interested in the low cost of the food. Additionally, for the project, we assumed that during the weekdays people order fast food to the office and due to the weekend days people order McDonald’s to home.

Hypothesis 1: The McDonalds in the office area will receive more orders during the working days than on weekends at lunchtime (13:00 – 15:00).

Hypothesis 2: The area with a high density of homes will have more orders during the weekends during lunchtime if to compare with working days.

Research Method

The first step of the research was to choose the office and home areas in Moscow. The data from Open Street Map was used in order to understand the number of offices, number of homes and population number around different McDonalds points. It is important to mention that before the incident with Yandex.Eda courier and while this project was in process the official webpage claimed that food can be delivered in 15 min after it cooked, however now the Yandex.Eda changed data and such information cannot be found. Nevertheless, we used Galton method to make the 15 minutes walking distance for the chosen areas.

The next step was an observation field study. During the lunch hours from 13:00 until 15:00 we counted the number of Yandex.Eda delivery guys in chosen locations. The observation took a week (from 13/05 until 19/05) in two locations.

Results and Discussion

As a result, the two McDonalds points were chosen: Lyublinskaya street, 165, building 1A (Picture 1, metro Maryino) and Presnenskaya embankment, 2 (Picture 2, Moscow City). The data suggest that Maryino does not have office buildings, however, Moscow City area has 26 office buildings. Additionally, according to Graph 1 the number of residential building is higher in Maryino area. The population number is summarised in Graph 2, and Maryino area of research has a lower number of residents if compare with Moscow City, additionally, Moscow City has 40 000 workers.

Picture 1. The McDonalds near metro Maryino in the periphery where residential houses are dominant
Picture 2. The McDonalds near Moscow City where the number of offices is higher than in other parts of Moscow

The field research took place from 13th of May until the 19th of May for the 7 days. The results are summarised in Table 1 and Graph 3. In summary, Moscow City received 1418 orders during the week, Maryino location only received 379 orders. According to the results, the highest number of orders was made on Friday (17/05) at the Moscow City area, however, the highest number of orders at the Maryino was counted on Sunday. 

Table 1. The results of the field research. The number of orders per day during lunch hours 13.00-15.00
Graph 3. The daily number of orders in Moscow City and Maryino area

The field research showed that during the weekends Marino McDonald’s restaurant full of visitors during the lunch hours if to compare it with the weekdays. Unfortunately, it was hard to make the same conclusion about Moscow City point as food court there have various restaurants and common tables so visitors are distributed around the floor.

In order to analyse the data, we still need to understand the population of each area during the office hours and during the weekends to understand a number of orders per capita. For this purpose, the additional calculation was made. We find out that the population of the houses which is in a 15 minutes walking distance in Moscow City is 27 000 plus 40 000 of office workers, in Marino 11 000 people is living in apartments, however there are no offices. According to Mosgorstats, 65% of the population can work, and 75% of this people are actually working, which means that 13 163 of people near the Moscow City are employed and 5 363 of Marino flat owners are employed as well. Therefore, 13 838 people do not work at the Moscow City area and 5 638 people have no job at the Marino point. Due to these calculations, we count the number of orders per capita which provided in Table 2.

Table 2. The number of orders per capita during working and weekend days in the Maryino and Moscow City area

According to our results, the highest number of orders per capita was at the Marino at weekends, also during the weekdays’ orders are higher than in Moscow City area. In Moscow City, McDonald’s orders are higher at weekends as well. Such results for the Marino point can be explained with observation data, as was mentioned above during the weekdays there were a few people at the restaurant, potentially not working population near the Marino point were order the food to home. A higher number of orders during the weekends can be explained with motivation spend time at home with family, it also can explain a higher number of orders at Moscow City point during the weekends. Additionally, such a trend can be explained with the fact that Marino location further from the center or entertainment locations, so local people prefer to stay at home rather than travel by metro. A low number of the orders during the working days at the Moscow City research area can be influenced by the fact that area has a variety of restaurants to order from nearby.

Conclusion and Limitations

Hypothesis 1 is wrong, the general number of orders are higher in Moscow City, however, if we look at the data per capita we see that Marino McDonalds receive more orders.

Hypothesis 2 is correct, Marino point has a higher number of orders during the weekend.

The main limitation of the research is that we were not able to provide clear information about the number of residents at the Moscow City and at the Marino point, the calculation was made by only using information from Open Street Map and analysed with Galton and Q-GIS. The next limitation is that data about the working population were calculated roughly according to statistics and may not represent a real picture.  Finally, for the researches, it was sometimes hard to calculate the number of Yandex.Eda couriers as movements were chaotic and some small miscalculations were probably made during the field research. 

If the experiment needs to be redone, we advise to include the calculation of the Delivery Club couriers to represent more realistic data about customer behavior.


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