1. BENVGSC4
Spatial Data Capture and Analysis
What’s for lunch?
An analysis of food habits and gentrification of
London
Duccio Aiazzi
Monday 6th
June, 2016
Abstract
Coined by Ruth Glass in 1964 in her book ”London: aspects of changes”, the word
”gentrification” refers to demographic shift in an urban community and it has spawned an
extensive field of urban research. Gentrification describes the process by which some urban
areas, traditionally inhabited by lower income class, start to be desirable by some pioneers
of the middle class or future middle class, attracted mainly by low rents. Gentrification
also causes a shift in the type of stores and services provided in the area, while long term
residents are progressively replaced by new comers. When this process is very fast, it can
lead to personal catastrophes such as forced displacement, eviction or homelessness but also
to subtler frictions between new and old residents, who belong to different cultures and
upbringings, and to a loss of social diversity.
”What’s for lunch” is an exploration of food habits of Londoners, depending on their income
and their area of residency. The rationale behind the project is that different social classes
have different consumption patterns, hence demand different types of retail. Based on the
Food Agency database, which contains the names of food premises in London, their geo-
location and their hygiene scores, we analyse the spatial concentration of different business
names, their correlation with income areas and we provide an interactive visualisation to be
published on the web.

2. BENVGSA3
GI Stystems and Science
A tool for the calculation of the compactness of
city blocks in R
January 11, 2016
Summary
The R function FUN SPDF Cmi Index.R calculates the index of compactness based on
the Second Moment of Inertia of a set of polygons. It has been developed in the con-
text of urban morphology, as a tool to be used in the analysis of street typology of
cities. The function takes as an input a set of polygons in the form of a SpatialPolygon-
DataFrame (SPDF), which is a class of elements of the package “sp”. The output is the
same SPDF with two added columns containing the area and the index of compactness
CMI of each polygon.
Word count : 1458
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3. CEGEG076
Spatio-Temporal Data Mining
Prediction of crime levels in Washington DC,
based on seasonality and census data
Duccio Aiazzi and Sarah Hank
Friday 1st
April, 2016
Abstract
In this study, we critically assess the performance of methods of machine-
learning with the aim of predicting areas of high and low seasonal burglary rates
in Washington, DC. We present two techniques: Random Forest (RF) and Support
Vector Machine (SVM). It is generally agreed that Random Forest and SVM are
amongst the best performing classifiers, and both have been used effectively in
crime classification. In our experiment, SVM performed better than Random For-
est by only a small margin which is likely not statistically significant. However,
Random Forest proved to be a better performer in terms of ease of implementation,
speed, and interpretability.

4. BENVGSC2
Urban System Theory
Is urban densification worth it?
Wednesday 23rd
December, 2015
Abstract
As a gross simplification, the debate about the urban form has revolved historically around the
centrists, who favour a compact, high-density city, and the decentrists, who argue for spread-out,
mostly residential suburbia organised around business cores. The decentrists’ position originates form
the industrial revolution when theorist argued for a residential model far from the congested and
polluted cities, immersed in nature and with abundant land availability. The centrists’ positions is
more recent and sprang from the realisation that cities are expanding at high rate into agricultural
and wild land. These different ways of imagining the ideal city, combined with the concern of how to
mitigate the effect of human activities on the climate and the environment, and a constant increase in
the share of population which lives in cities, have fostered spirited debates.
As urban sprawl is mostly considered detrimental to the environment and to the efficient function-
ing of the city, most administrations have adopted policies to push for densification and brown-field
development. The major debate revolves around the optimal urban density in order to guarantee pub-
lic transports and services and to reduce the consumption of agricultural and wild land. But there
are critics who point at the likely increase in traffic congestion and green space reduction, the lack of
evidence about sustainability in favour of densification and in general the right of people to choose
where and how to live.
In this essay, I first describe urban sprawl and the alternative proposed by the centrists - densifica-
tion. I then discuss the definition of density that is used both in the literature and in practice. Next, I
discuss how urban sprawl and densification impact transports, land conservation and social equality
and draw some conclusions.
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5. BENVGSC3
Smart Cities: Context, Policy and Government
Assessing smart cities:
how smart is Paris?
Friday 1st
April, 2016
Abstract
In this study, I asses the extent to which Paris, the capital of France, can be considered ”smart”.
I first review the literature related to the concept of smart city and provide an operative definition
based on five indicators, in order to frame the scope of the work. I then asses the ”smartness”
of Paris by looking at how new technologies such as mobile phones, internet and internet of
things are affecting these five indicators. I found that Paris has a lively eco-system of startups
and an innovative administration often at the avant-guard of transport technologies. The public
administration is a major actor in the promotion of new technologies and even in supporting of
startups and has been traditionally quite fast in experimenting new technologies. Some concerns
over privacy are slowing down the adoption of some technologies but they also spark interesting
debates. The city administration is currently highly committed to increasing interaction with the
citizens and extending participation.

6. BENVGSC2
Quantitative Methods
Hourly fares and pick-up probabilities for NYC taxi
drivers
Duccio Aiazzi
Wednesday 9th
December, 2015
Abstract
In this essay I investigate the hourly fare of the drivers of the Yellow Cabs in New York, using the
NYC Taxis Trip Data 2013 obtained by Chris Whong following a FOIL (Freedom for Information Law)
request. The hourly fare is calculated taking into account the time spent in a trip as well as the time
spent idle after the trip, looking for a customer. The choices available to the taxi driver to maximise the
hourly fare relate to time and location: the time of the day, the day of the week, and the starting area. I
analyse the influence of these variables and their interaction on the hourly fare using categorical data
and linear regression. From the analysis, the most important factor influencing the hourly fare is the
location. A taxi driver can choose the starting location but during the rest of the day the location will
depend partially on where the trips lead. In the second part of the essay, I develop a tool to calculate
the probability of a pick-up depending on the date, the time interval and the location, so that after
a drop-off a taxi driver can have a better overview of where to go next and what is the route that
maximises the probability of a pick-up throughout the journey.