Author: Bin

Journal Article

• Shedding new light on residential property price variation in England: A multi-scale exploration
• Delineating the spatio-temporal pattern of house price variation by local authority in England: 2009 to 2016
• A new attribute-linked residential property price dataset for England and Wales, 2011 to 2019

CASA working paper/ Research briefing notes

• Creating a new dataset to analyse house prices in England
• Shedding new light on residential property price variation in England: a multiscale exploration

This working paper has been published in Environment and Planning B: Urban Analytics and City Science.

• Delineating the spatio-temporal pattern of local authority house prices variation in England between 2009 and 2016

This working paper has been published in Geographical Analysis.

• Exploring local authority travel time to London effects on spatio-temporal pattern of local authority house prices variation in England
• Linking administrative and consumer data to understand housing space consumption in England and Wales

Data repository

• A new attribute-linked residential property price dataset for England and Wales 2011-2019
• House Price per Square Metre in England and Wales

Reproducible work

• Linking Land Registry Price Paid Data (PPD) and Domestic Energy Performance Certificates (EPCs)
• Customizing ggplot2 plots in R for scientific research 
• Working with large datasets in R
• Fast reading multiple EPC csv files together in R

Conference

• GISRUK 2018: A method for representative house prices for small areas though Data linking Land Registry Price Paid Data and OS MasterMap
• GISRUK 2019: A new insight into residential house price variation across England through linking Land Registry Price Paid Data and Domestic Energy Performance Certificates
• GISRUK 2020: Delineating the Spatio-temporal Patterns of House Price Variation at Local Authority Level in England
• GISRUK 2021: Understanding housing affordability to determine the best property search areas for homeowners moving out of London
• GISRUK 2021: The use of Linked Consumer Registers to understand social and residential mobility

Data Scientist skills

1. A Learning Path To Becoming a Data Scientist
2. R powered web applications with Shiny (a tutorial and cheat sheet with 40 example apps)
3. Data Professor: Free Data Science Courses
4. Modelling Count Data in A Multilevel Framework
5. How to create Chord Diagram using R | Data Science
6. Add image & graph popups as tooltips
7. R: animation map
8. Using ggfx for good
9. A ggplot2 Tutorial for Beautiful Plotting in R
10. Introduction to social network analysis with R
11. Animating Your Data Visualizations Like a Boss Using R
12. DPLYR TUTORIAL : DATA MANIPULATION (50 EXAMPLES)
13. Data Visualization with R
14. A Detailed Guide to ggplot colors
15. Hybrid hierarchical k-means clustering
16. England and Wales commute data
17. Some of the best data visualisation content encountered during December 2019
18. 5 ways to measure running time of R code
19. An animation of the public transport network
20. Spatial data science for sustainable development 
21. spatial optimization

English

1. Learn English with Gill (engVid)
2. BBC Learning English
3. Learn English with Emma (engVid)
4. Learn English with Rebecca (engVid)

Articles

1. A Multilevel Analysis of the Variations in Domestic Property Prices: Southern England, 1980–87
2. Multi-Level Models for Geographical Research
3. Specifying and Estimating Multi-Level Models for Geographical Research
4. Why geography matters
5. Recent Empirical Work on the Determinants of Relative House Prices
6. Standards in housing design: A review of the main changes since the Parker Morris report (1961)

Training course

1. Statistics for Researchers Workshop, UCL
2. Multilevel Models: Practical Applications,
3. Multilevel Modelling, RSS
4. An introduction to spatio-temporal modelling of small-area data in R
5. Using UK data portals to find and evaluate data for social science research
6. Introduction to Spatial Interaction Modelling, NCRM
7. Interactive Visualisation in R, RSS
8. Big Data: tools and statistical methods, NCRM
9. Computational Methods for Social Data Science: Exploring Society through Visualization and Modelling
10. Spatial Econometrics

Interesting tweet

(1) PhD research

1. Planning and Housing Landscape Review – Executive Summary
2. Market Commentary – 2020 Review
3. Interest rates and Bank Rate
4. The space standard in Overcrowded housing(England)
5. OECD-modified scale
6. What is multilevel modelling? by Professor Kelvyn Jones
7. 527 swimming pools built in London
8. Bringing together housing, land and planning geospatial datasets
9. Housing Affordability Calculator
10. Rail fares: Commuters ‘pay fifth of salary’ on season ticket
11. Applying Spatial Analysis to Real Estate Decision-Making
12. The Role of Data Science in Real Estate
13. Together at last – UK’s planning and housing statistics now in one place
14. How far is too far? The distance workers commute to cities – mapped
15. London’s most affordable commuter towns
16. Our guide to house price growth vs commute cost
17. Average London House Price vs London Commuters by Workplace
18. Why should I commute to work and move out of London?
19. Big house price savings if you’re prepared to commute
20. House prices in commuter towns around London substantially cheaper
21. What benefits are there for London commuters moving to Surrey?
22. London’s commuter towns More than just house prices and travel times
23. Long-distance London commuters push up local house prices
24. Housing crisis: Where are the most new homes being built?
25. City Definition
26. Admin-based statistics for property floor space, feasibility research: England and Wales
27. Help Shape Our Future:The 2021 Census of Population and Housing
28. About the size of a London flat
29. Address matching: Machine learning innovations in address matching: A practical comparison of word2vec and CRFs
30. London Commuter Belt (East)/M11 Sub-Region Strategic Housing Market Assessment 2008
31. The 10 cheapest commuter towns within an hour from central London
32. Britain’s new towns
33. Derbyshire becomes a house price hotspot – and it’s all because of London
34. The English New Towns since 1946
35. the 31 hotspots where house prices will surge now
36. Oxford vs Cambridge: House price rises in UK’s least affordable cities

(2) R

1. ggraph – exploring flows
2. Create geographic flow maps : FlowMap.Blue
3. Global Migrant Stock Chord Diagrams
4. Travel flows
5. COVID_Cases_By_Age
6. FiveThirtyEight
7. Difference between subset and filter from dplyr
8. 5 Ways to Subset a Data Frame in R
9. officer R package
10. Tidyexplain: Tidy Animated Verbs
11. Beginners guide to Highchart Visual in R
12. htmlwidgets for R
13. Interactive plots with ggiraph
14. xAxis | highcharts API Reference
15. Single-Page Reference | R | Plotly
16. layout | R | Plotly
17. RStudio Cheatsheets

(3) Other

1. Bivariate Choropleth Maps: A How-to Guide
2. Affordable housing monitoring
3. Where are the largest cities in Britain? 
4. BBC Briefing on housing
5. Local housing supply by postcode
6. Where are all the UK’s new homes being built?
7. Bus/tram/metro stops in GB
8. The 50 Richest Women in 2021
9. Journey times to assess equity of access to services in England.
10. A chord diagram of travel to work flows in Inner London
11. The interrelationships between all scientific disciplines
12. Vaccinations sites in England
13. Median Council Tax bands in England and Wales
14. Milton Keynes growth mapped by the year the postcode was introduced 1980-2020
15. INSPIRE Index Polygons in the West Bridgford area
16. Motorways and A roads of GB
17. New open building and population density datasets
18. Nighttime lights
19. Potential land constraints
20. pavement widths
21. Green spaces in residential gardens
22. Care homes locations
23. Retail Catchment Flows from Geolytix
24. Open data of UK Supermarket Retail Points (Geolytix)
25. Two thirds of UK homes ‘fail on energy efficiency targets’
26. Where are the UK’s youngest and oldest city populations?
27. Raw number of airbnb listings in each area
28. Commute data in England and Wales (Alasdair Rae)
29. A new analysis of the extent of short-term and holiday letting services (including but not limited to Airbnb) in London
30. Over one in five people in the UK live in poverty

On-line books

1. Urban Informatics
2. Mastering Software Development in R
3. R Markdown: The Definitive Guide
4. Big Book of R
5. Systems approaches to social, economic and geographic research
6. Spatial Modelling for Data Scientists
7. Transport Access Manual: A Guide for Measuring Connection between People and Places
8. Essentials of Geographic Information Systems
9. Interactive web-based data visualization with R, plotly, and shiny

Report/blog

1. New Urban Agenda: New Urban Analytics

2. Housing development: the economics of small sites – the effect of project size on the cost of housing construction, BCIS
3. Delivering more homes and better places
4. Predicting House Prices with Machine Learning
5. Using R for Spatial Data Analysis
6. House price calculator: Where can I afford to rent or buy?
7. Modelling the distribution of mortgage debt
8. IS THERE A BETTER WAY TO MEASURE HOUSING AFFORDABILITY?
9. Planning for the future

Other

1. Resources for PhD Students
2. Glitch: Digital literacy
3. What is a Flowchart
4. 10 Amazing Windows Shortcuts
5. Using Big Data to Solve Economic and Social Problems
6. Supporting housing delivery and public service infrastructure
7. Everyone should be able to access a safe and affordable home. Increasing housing supply is essential to creating a fairer, more affordable housing market and boosting productivity across the country.

Data linkage

Combine all the published EPCs datasets and geo-reference them with UPRN information.

Interactive web apps

Creating a Shiny app to reflect the housing affordability result in my PhD research

1.Spatial coverage

The study area is the whole of England, one of the countries of the United Kingdom. It contains nine regions: the North East, the North West, Yorkshire and the Humber, East Midlands, West Midlands, East of England, the South East, the South West and London. Administratively, England is divided into 326 local authority districts. The extent of the study area is presented in Figure 1.

Figure 1 The study area of this research

2.Time coverage

Limited by the time coverage of my new linked house price in England, my research focuses on the time period between 2009 and 2016.

1.Land Registry datasets

• Land Registry house price data

Transaction price, transaction date, property type.

• INSPIRE Index Polygons spatial data

Building plot

2.Ordnance Survey (OS) datasets

• OS AddressBase Plus
• OS MasterMap Topography Layer
• OS MasterMap Integrated Transport Network (ITN) Layer
• OS VectorMap District (VMD)
• OS Building Heights

• OS MasterMap Sites Layer

3.Ministry of Housing, Communities & Local Government (MHCLG) datasets

• Domestic Energy Performance Certificates (Domestic EPCs)
• English Housing Survey
• English Local Authority Green Belt Dataset

• English indices of deprivation 

4. ONS Open Data

• National Statistics Postcode Lookup (NSPL)
• Census 2011

5. The Department for Work and Pensions (DWP) datasets

• Family Resources Survey

6.Other dataset

• Zoopla dataset in England (2010-2017)
• Stamp Duty rates
• Geolytix retail points GeoData
• GetTheData: Open Flood Risk by Postcode
• Flood Risk Areas
• Schools in England
• School establishment data

• State-funded school inspections and outcomes

• National Public Transport Access Nodes (NaPTAN)
• GB Rail GTFS (General Transit Feed Specification) data
• Estimates of station usage 2017-18 (11 December 2018 version)
• The national rail route diagram map(June 2019)
• The national rail train operators map
• London Connections Map
• Estimates of station usage 2017-18
• London Tube Map (December 2019)
• Train fares data
• The areas reachable from each railway station in England at 7a.m. within 15 minutes, 30 minutes, 60 minutes, 75 minutes, 90 minutes, 105 minutes, and 120 minutes, by public transport were extracted from the TravelTime Platform

Hello! I am Bin Chi, a PhD researcher at The Bartlett Centre for Advanced Spatial Analysis (CASA), University College London. I have a background in both GIS and urban planning (Land Use Management), with a particular emphasis upon using statistical modelling and geographical analysis to investigate the spatio-temporal pattern of socio-economic processes to further guide local planning policy.

During my PhD research at UCL CASA, I have built up a consistent framework to coordinate and integrate analyses across different spatial and temporal scales of England’s residential house prices to advance understanding of the housing affordability issue. As my PhD is almost completed, the blog here is a brief summary of my research findings. All the blogs are a team effort, which has been supported by Adam, Thomas and Robin.

If you have any questions, please feel free to get in touch.