Dissertation methodology

Introduction

This document is an appendix to the dissertation and is intended mostly for personal use and to keep track of what is being done. While the general aspects of the final work are still to be determined and done, as of now the following will give an overview of the ideas that are intended to be achieved. The system of interest is the neighborhood, as of now, it is defined to be as set of streets (edges in a spatial network), around which are located land and infrastructure of various use. A neighbourhood is then a set of streets that link together these various elements that are necessary in ones life. Each neighbourhood is unique in a sense and similar to all the others in another. The differences can be due to the local population (ethnicity, age, wealth, cultural elements, historical context), land use policies, geographical context(hills, near a park or a river), theese are likely to be the parameters that differ from one place to the other. For example, the St. Katharine Docks marina on the thames in London can be called a neighborhood and is quite different from the other neighborhoods in london for it’s geographical context, it is a port, and the lively parts of the area are located around it, the port hosts big yachts, this gives an idea of the wealth that the people of this place have, and to accomodate and answer their needs, the neighborhood houses various amenities, like shops, restaurants, pubs, galleries, schools, community centres, religious places, banks , public transport etc… The importance of studying neighbourhood is that it represents in a way the most fundamental element of the structure of a city. Historically cities grow out of smaller villages by expanding their area and thus capturing the neighbouring villages in the process. London is the perfect illustration of this phenomena with different parts of the city bearing the names of the villages that used to be here. And while the physical limits have vanished under the constructions and developments that have been occuring other the centuries, the village like feel of a lot of the areas is still present. Each of these areas has it’s own set of amenities that contribute to the way we experience it and the reasons we go there. So what is a neighbourhood ? From the perspective of a citizen, it is the first environment one sees when leaving his house, the first interaction with the city that one has when leaving home. It seems natural to think that this environment has an influence on the individuals that live in it. Modern science of cities has the potential to measure that feeling and it’s relation to the quality of the surrounding built environment. Thanks to the extensive amounts of data that human activity generates, it is now possible to rely on a more scientifically grounded argumet when dealing with issues of the city. The impact of the built environment on health has wide and growing attention and some occurenses of illnesses, mental or physicall, have been linked to the environment in which individuals live. Some very comprehensive researches done on the north american continent have found links between the diversity of availbale transport modes and the obesity and diabetes rates. Individuals living in areas segregated form the point of view of transport and relying mostly on cars to move around are more likely to develop diabetes and be obese. On the other hand, those living in areas where a diversity of modes of transports is available and the the environment is active travel friendly, where showing a better health condition on average. The use of car has also proven to be not only harmfull for it’s drivers health, but also the people around. By studying pollution from vehicles in geneva, (ref aux gars de l’epfl) have found similarities between pollution clusters and Parkinson desease in people living within them.
In this work, the ambition is to study the diversty and location of amenities using various existing metrics that will be described below, and investigate their “connectedness” on the level of the neighborhood. After understanding the spatial distributions of the amenities, I would like to try and define what is a neighborhood in a stricter way. Using these results, I then would like to ananlyze the street network, and take a look at it from the perspective of New Urbanism. This means trends that propose a human centered urban environment, as opposed to a car centric one, which would facilitate a mentally and physically healthy lifestyle. On the level of the street, the steps that can be done by policy makers, planners, builders, to respond to these new needs are addressed by analyzing the street network and proposing the road segements that would on the neighbourhood level facilitate active travel. Cities are gradually кeducing the dependence on cars and providing the opportunity to move around safely, actively and in a beautiful environment could be a strong move towards car use reduction on the neighborhood level.

Literature review

The motivation to take a closer look on the neighbourhood comes from the fact that it has a pretty broad definition in most of the literature, while it’s importance in the personal experinece and as a unit to understand the city better seems to be crucial. Without the ambition of diving into the modifieable area unit problem (MAUP), this research attempts at taking a look at the city from a user, citizen perspective and define neighbourhoods as areas that would include the diversity of lifestyles that each of us want to have. A double approach is chosen here, on the one hand, the main predictor for a neighbourhood is assumed to be a spatial distribution of amenities that, the resident interacts with. On the other hand, using a comprehensive data set of mobile GPS signals, the preferential occupation areas of individuals outside of their residence and work places are investigated. It seems as if each of us has his own undestanding of the concept of neighbourhood. A more precise definition of a neighbourhood is often left out of the discussion in the literature, but the term is used thouroughly. Most often for efficiency reasons, to save computation cost for example, the city as a system of interest is divided into regular areas that are easy to investigate, usually, 1x1 \(km\) or 1.5x1.5 \(km\) squares and aggregate values over these areas are then used. However, when attempting to understand the city on a more granular level, it is intresting to ask the question of what is actually a neighbourhood ? Be more rigurous towards the way we define the unit of our system. Is there a way to give a more formal definition, which would be based on some intuitive notions and grounded in an argument supported by data and investigation. The approach chosen here to define a neighbourhood is to take a look at it from the “user”, or the citizen perspective. The term neighbourhood is linked to proximity, the area surrounding ones home. And everything that is within this area will be defining in how we percieve this place. In other words, the various land uses, amenities that can be found within the neighbourhood are going to determine the quality of our interaction with it.

Urban science and planning

The general trends in planning are switching from a car centric city, to a more human centered city. This switching attitude is supported by various research that spans from the field of urban sciences, to economics, to social sciences. It is becoming the central paradigm of planners work and is resumed by the New Urbanism theory (Iravani and Rao 2020). At the center of the arguments are found inclusivity, well-being and sustainability. An environment that is more inclusive must support a variety of individuals that interact with it and in it. The inclusivity is answered by the wide use of participatory planning, where the citizen, as the end user, is given the chance to articulate his needs and point out the existing problems. This sort of approach has allowed answering the needs of a wider audience at the stage of project design and has lead to better experience of the final outcome. Well being is answered by prioritizing individual health and developing designs that encourage and promote an active lifestyle. Sustainability is reached through facilitating public transport use, active travel and green infrastructure. Nature is being actively brought back within cities through a more considerate urban design, which is less car centered.

Getting a better understanding of the neighbourhood can be important for various aspects of urban research and planning. From investigating health-impacting factors of the environment (Frederick, Riggs, and Gilderbloom 2018,glazier_density_2014,@creatore_association_2016,fleury_geospatial_2021), to building models of amenities in neighbourhoods (Hidalgo, Castañer, and Sevtsuk 2020), investigating their accesibility (Xu et al. 2020).

It seems now, that city residents are facing some radical changes, partially accelerated by the coronavirus pandemic, which is likely to transform urban life in the near future. Various measures were put in place during the pandemic to reduce contamination rates, such as extended terraces in restaurants and bars, streets converted to a pedestrian-only acces, spaces converted for outdoors leisure and entertainment. Many cities where these measures where in place are now considering keeping them. Partly as a consequence of public opinion in favor of it, and partly because commercial activity benefited from these measures. Restaurants and shops in streets that were converted for pedestrian use only saw their benefits either remain stable or increase compared to pre-pandemic levels.

A stronger link between how the city is studied in the academic circles and the planning world could lead to more impactful results that ultimately ameliorate the built environment for the people.

Defining neighbourhoods

Methodology

Analyzing the amenities

Entropy of amenities: Overview

In order to analyze the available amenities, in a defined neighbourhood, the Shannon entropy index is chosen as the indicator of diversity. It is defined as follows:

\(E = -\sum_{i=0}^{N}p_iln(p_i)\) where \(p_i\) is the probability of the system to occupy state \(i\). When applied to the neighborhood, we investigate the number of different amenities. We define our index as follows: let \(A_j = M_{:,j}\) be the number of amenities of type \(j\) in the city, let \(m_{ij}\) be the number of amenities of type j in area i. The share of amenities of type j that are in area i is thus \(p_{i,j}=\frac{m_{i,j}}{M_j}\), the entropy of area i is thus defined to be \(E_i=-\sum_{j}p_{i,j}ln(p_{ij})\). We have \(M_j=\sum_{i}m_{i,j}\) and \(N_i = M_{i,:}=\sum_{j}m_{i,j}\) the total number of amenities in neighbourhood i.

Entropy of amenities: Categorical index

To build a model that could allow to investigate and compare neighbourhoods in a more efficient way and understand the profiles of the neighbourhood, the amenities are grouped categorically. Using the knowledge of groups and number of amenities, a model can be built that illustrates the profile of the neighbourhood based on amenities, for example some neighbourhoods can have a wide choice of restaurants, but be missing cultural amenities like galleries or museums.

We can thus define the matrix \(M\) where each element is \(m_{i,j}\) of the number of amenities of each type in each area.

Following the methodology of Cottineau and Arcaute (2020) , we can investigate similarities with respect to the distribution of amenities using the cosine similarity. It is defined as follows: \(cos(k,p)=\frac{A_kA_p}{|A_k||A_p|}\) where k and p are indexes of areas and \(A_k\) and \(A_p\) are row vectors of their respective parameters that are compared. \(A_kA_p = m_{k,j}m_{p,j}\) is the vector product. From this we can investigate the areas, this can inform us about the lifestyle of people there. More restaurants/bars/clubs for example would mean that it is an area with a lively nightlife. Similar distributions of amenities across areas can then be compared to such indicators as deprivation index, crime rates.

Given how many different amenities there are in the OSM data base, it can be useful as well to introduce a more detailed index. Again using Shanons definition of entropy and introducing categories of amenities, such as safety (police, fire), health (Hospitals, GP practices etc), essentials (groceries, retail, markets), culture (cinemas, art galleries, museums), leisure (restaurants, bars). Let’s say that we have \(N_c\) categories, we define \(ln(N_{ic})\) as the entropy of the number of categories of amenities in an area \(i\). And we define \(SEI_i^c = ln(N_{ic}) - \sum_j p_{ij}^c ln(p_{ij}^c)\) where \(p_{ij}^c=m_{ij}/M^c_i\). \(M_i^c\) is the number of amenities of category \(c\) present in \(i\). Technically speaking this index gives us the quantity of information that is present at location \(i\), which can be interpreted as diversity.

A first test on the borough of Haringey where we defined the neighbourhoods as Voronoi polygons around public transport stations of the borough and assigned each amenity to it’s area.

References

Cottineau, Clémentine, and Elsa Arcaute. 2020. “The Nested Structure of Urban Business Clusters.” Applied Network Science 5 (1): 2. https://doi.org/10.1007/s41109-019-0246-9.

Creatore, Maria I., Richard H. Glazier, Rahim Moineddin, Ghazal S. Fazli, Ashley Johns, Peter Gozdyra, Flora I. Matheson, et al. 2016. “Association of Neighborhood Walkability with Change in Overweight, Obesity, and Diabetes.” JAMA 315 (20): 2211. https://doi.org/10.1001/jama.2016.5898.

Frederick, Chad, William Riggs, and John Hans Gilderbloom. 2018. “Commute Mode Diversity and Public Health: A Multivariate Analysis of 148 US Cities.” International Journal of Sustainable Transportation 12 (1): 1–11. https://doi.org/10.1080/15568318.2017.1321705.

Hidalgo, César A., Elisa Castañer, and Andres Sevtsuk. 2020. “The Amenity Mix of Urban Neighborhoods.” Habitat International 106 (December): 102205. https://doi.org/10.1016/j.habitatint.2020.102205.

Iravani, Hamid, and Venkat Rao. 2020. “The Effects of New Urbanism on Public Health.” Journal of Urban Design 25 (2): 218–35. https://doi.org/10.1080/13574809.2018.1554997.

Xu, Yanyan, Luis E. Olmos, Sofiane Abbar, and Marta C. González. 2020. “Deconstructing Laws of Accessibility and Facility Distribution in Cities.” Science Advances 6 (37): eabb4112. https://doi.org/10.1126/sciadv.abb4112.