Ruby Johnson Spatial Data Science
Portfolio
About
With a First Class Honours in Human Geography with GeoComputation and Spatial Analysis, I have recently completed a Masters in Spatial Data Science and Visualisation MSc, from the Centre for Advanced Spatial Studies, University College London.
Over the past four years, I have gained experience in computational techniques and languages, applied to delivering insights from big data. From wrangling data to quantitative methods, such as spatial analysis, machine learning, and visualisation, to presenting results effectively in reports and presentations I have successfully applied these techniques to a range of geospatial problems.
Skills
Python (Geopandas, Folium, Keras, Matplotlib, NumPy, Pandas, PySal, SciKit, Shapely, TensorFlow), R, NetLogo, Google Earth Engine (JavaScript), SQL, CSS, Javascript (D3 js, Mapbox, Leaflet JS, ECharts, Chart js), HTML, ArcGIS, QGIS, Juypter Notebook, RStudio, Markdown, Anaconda and Docker
Profiles
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Contents
Thesis: Identifying and Predicting Rural
Gentrification
Digital Visulisation: UK Viticulture
Digital Visualisation: Ukrainian Refugee Crisis
GIS: Accessibility to Green Space
Data Science for Spatial Systems: Street
Lights and Crime
Centre for Urban Progress Hackathon: Effective
Emergency Responses
Applied Spatial Science: Air Pollution throughout Covid-19 Lockdowns
Environmental Remote Sensing: Singapore's Land Surface
Identifying and Predicting Rural Gentrification
ntrification poses one of the greatest threats to pulations, with many now facing displacement m their own neighbourhoods. Despite this, the ability to mitigate and manage gentrification is plagued by subtlety, causing a lack of quantitative delamination.
This distinction-awarded project sought to develop the ability to identify and predict gentrification, in three ways; it extended the boundaries of analysis beyond hyper-urban spaces, instead focusing on the South-East county of Kent. Following this, the ability to identify current trends is often hampered by data availability. As such, data for analysis compared traditional census measures to sources of NowCasting Finally, the need to correctly identify gentrification and distinguish it from other types of neighbourhood change was expanded, as shown in the flow chart.
These aims were met by synchronizing a feedforward workflow, combining a variety of machinelearning algorithms, with 200 categories of data used. Methods included using Principle Component Analysis to create a composite index score, representing the socioeconomic state of a local area Domains were created and inputted into K-Means clustering to form geodemographic categories Finally, supervised machine learning algorithms were compared and evaluated to develop an efficient method of identification and prediction.
| PYTHON | MACHINE LEARNING CLICKTOVIEWCOD E
THESIS
Neighbourhood State Score
Ascending Descending Stable
Census Data
Rural Repopulation
NowCasting Data
Rural Repopulation
Rural Depopulation
Gentrification
Rural Depopulation
Gentrification
Mainstream
Gentrification
Marginal
Gentrification
Super Gentrification
Rural Repopulation
Rural Depopulation
Gentrification
Thanet
Rural Repopulation Canterbury
Super Gentrification
Marginal Gentrification
Mainstream Gentrification
UK Viticulture
DIGITAL VISUALISATION | HTML | CSS | JS | MAPBOX
Based upon the theme of local to global, I worked with a team of varying skill sets to successfully create interactive visualizations examining UK viticulture Built around a narrative of climate change, the website guides a viewer through several stages; From global changes in climate to locations of vineyards within the UK and temporal market trends to county climatic patterns, the user ends on a single vineyard.
Maps were created using Mapbox Studio and then integrated using Mapbox GL JS The visualisation allows users to hover over different vineyards or counties, detailing essential information. Following this, climatic patterns comparing the South-East and the UK illustrated the ideal weather patterns and were created using Chart JS
C L I
CKTOVIEWWEBS I T E
CKTOVIEWWEBS I T E
Ukraine Refugee Crisis
These visualisations were created in response to the Russian invasion of Ukraine, poised to follow a refugee's journey At the time, 3 2 million people had fled the country, with news stories centred on visa rules.
As the user clicks, their viewpoint is changed: From beginning in Ukraine, the user can examine how many displaced persons from each Oblast (national administrative unit of Ukraine) there are, displaying the tensions on the eastern border with Russia. Then the viewpoint pans to each border, displaying border crossing points The user can discover this information by hovering over the layer Thereafter, the view zooms out to show all neighbouring countries. Displayed are proportional circles with the intake of refugees Finally, as the view shows Europe, the user can hover over different countries, highlighting visa rules for each country in response to the Ukraine refugee crisis
C L I
DIGITAL VISUALISATION | HTML | CSS | JS | MAPBOX
T
Accessibility to Green Space
GEOGRAPHIC INFOMATION SYSTEMS AND SCIENCE | R
Tasked with a brief of examining access to green space, in relation to sociodemographic factors, I chose to focus on income as a proxy of deprivation, and the percentage of healthy parkland cover. With no actual data on green space, data was collected from the Landsat 8 satellite This was used to calculate the Normalised Difference Vegetation Index, and then the percentage of park cover in administrative units. I created both maps and completed statistical tests to investigate the brief
The notable element of this work is that it was produced in exam conditions, within 8 hours. The analysis concluded that more affluent populations did have more access to healthier, larger green spaces, than those living below the poverty line.
C L I CKTOVIEWREPOR
Bivariate map of Adelaide showing median weekly income and percentage park cover
Higher inco H i g h e r p a r k c o v e r a g e
Street Lights and Crime, Lambeth
DATA SCIENCE FOR SPATIAL SYSTEMS
light of the Sarah Everard case, and the resulting vernment promise to fund more street lights, I focused project on deciphering if there was any statistical nificance between street lights and crime rates within Lambeth A key difficulty was the methodological approach; previous studies had access to restricted data or took a temporal comparative stance. The resulting analysis was based on multiple linear regression and conducting
CLICKTOVIEWCOD
E 5
Results of T-Test
Results of Odds Ratio
Crime hotspot map Street light and crime locations
Centre for Urban Progress Hackathon
WINNER | BEST OVERALL PROJECT
This week-long hackathon, organised by the Centre for Urban Science and Progress, King’s College London, offered the choice of three briefs. My team, comprised of CASA students, opted to analyse 250 data sets from Public Health England, detailing gas diffusion in different simulations of an attack in Manchester Our aim was to offer solutions for effective emergency response. Working together, our resulting conclusions and presentation garnered the award of Best Overall Project. Beating competitors from New York University, Glasgow and King's College London, the panel complimented our comprehensive, yet communicative presentation.
Please note, maps were co-produced with teammates
Best Case Scenario
North-East winds, with High Magnitude
Worse Case Scenario
Easterly winds, with Low Magnitude
Air Pollution and Covid Lockdowns
APPLIED SPATIAL SCIENCE
Produced at a time coinciding with the Covid-19 national lockdowns, I poised this project to focus on the supposed only positive: namely, recorded reductions in air pollution, witnessed globally. While many studies had used ground sensors, these are restricted by local fluctuations, and rarely offer a holistic view of air pollution
As such, I opted to utilise Nitrogen Dioxide readings, collected from the TROPOMI sensor, aboard the Sentinel-5 Precursor The sensor offers high spatial resolution, at 1 11km, with the passive sensor measuring radiation from the top of the atmosphere. After collecting data from Google Earth Engine, allowing for median values over the course of each lockdown to be calculated, percentage change maps were calculated
Compared to the baseline of the year before, an unsurprising statistically significant reduction in NO2 was witnessed throughout London for all lockdowns Although these trends, and driving factors, are uncertain, it is indisputable that the observed results are caused by drastic alterations in anthropological behaviour Whilst the bettering of air quality is short-lived, these findings reveal answers to one of the greatest environmental challenges facing populations..
Mean Nitrogen Dioxide levels throughout first national lockdown (2020) Mean Nitrogen Dioxide levels throughout year before the first national lockdown (2019)
Singapore's Land Cover
ENVIRONMENTAL REMOTE SENSING
This work is centred on Singapore, with raw satellite data collected and wrangled in Google Earth Engine and visualised using QGIS Data from the Sentiel-2 Multispectral Level 2 sensor was utilised to produce a true colour composite image, from visible light and infrared bands, and the QA60 cloud masking band (bands 2-8, 11-12).
The classification map was produced using random forest categorization. Initial unsupervised cluster maps were created to uncover expected patterns. Supervised classification produced the final image: 150 geometry points, referenced by the true colour image, formed the training points for each land cover.
The resulting map displays that urban land cover, combined with impervious surfaces, covers 54% of the country, whilst vegetation covers just 26%. This difference is caused by Singapore's rapid urbanisation since the 1950’s, with 100% of the population now defined as urban The rapid population growth, from 1 million in 1950 to 5 million in 2010, has caused many environmental issues, such as temperature increases in industrial areas and losses of biodiversity
