ucl department of geography censusgiv geographic information visualisation of census data pablo...

UCL DEPARTMENT OF GEOGRAPHYUCL DEPARTMENT OF GEOGRAPHYUCL DEPARTMENT OF GEOGRAPHY

CensusGIVGeographic Information Visualisation of Census Data

Pablo Mateos

Oliver O’Brien

Department of Geography

University College London

CASA Seminar9 December 2009

www.censusprofiler.org

UCL DEPARTMENT OF GEOGRAPHY

Contents

• Context & Justification• CensusGIV Aims &

Objectives• Design Considerations• System Architecture• Demo


Context & Justification


The Generation

• Those born after 1993 have only known life with the Internet– A generation whose first port of call for knowledge is the

internet through Google’s search engine, as opposed to books, libraries or traditional (off-line) information sources

(CIBER, 2008)


Moving Beyond “Traditional Web-GIS”


Geographic Visualisation at UCL

• www.londonprofiler.org• www.maptube.org• www.publicprofiler.org/WorldNames• www.nationaltrustnames.org• atlas.publicprofiler.org

• Coming soon www.censusprofiler.org


London Profiler – KML Search & Feeds


Geoweb 2.0 in Teaching

UCL Geography undergraduate field course in London


Geovisualisation (GVis)

• Refers to the visual representation of spatial data. • GVis as a research tool use for:

– Hypotheses generation, knowledge discovery, analysis, presentation and evaluation (Buckley, 2000)

• Increasing realisation of the potential for ‘geography’ to provide the primary basis for innovative visualisation and knowledge exploration

(Dodge, McDerby and Turner, 2006)

• Recognised potential of GVis – To make sense of increasingly large datasets– Produce alternative representations of space


Current Census Thematic Maps by ONS

• Neighbourhood Statistics (NeSS)– 11 steps to

view a census thematic map!

• Mapping in CASWEB – not present


NeSS maps via SVG/ Flex applications


The need for Census mapping is clear!


gCensus: A First Approach

• Query-based KML maps of 2000 US Census variables• http://gecensus.stanford.edu


CensusGIV Aims & Objectives


CensusGIV: Objectives

1. Develop a prototype to provide innovative geographical visualization of the Census small area statistics datasets.

2. Provide an extensive technical evaluation of the different technological alternatives.

3. Proposal to scale up to a full service in 2011.

4. Promote the use of innovative geographic visualisation of population datasets using mapping mashups.


CensusGIV: Plan

• ESRC Census Development grant £80,000• Timeframe: 15 months (2009/10)• Develop a Geovisualisation prototype of the UK

2011 Census using “Geoweb 2.0” technologies• Mapping mashups based on data feeds from an

ONS “Census hypercube” or NeSS data stream


People

• UCL Geography– Pablo Mateos (P.I.)– Paul Longley (co-P.I.)– Oliver O’Brien

• UCL CASA– Mike Batty (co-P.I.)– Richard Milton (consultant)

• User Panel– Jointly with EDINA DIaD project


CensusGIV: Requirements and Issues

• User not faced with queries or complex questions!– Start with a map (e.g. population density)– Automatic scale-determined geographical units– Base map backdrop

• Available to the general public & “mashable”• Issues:

– Intellectual Property Rights • Geographic boundaries & Census datasets

– Data size: Over 3,000 Census variables x 300k geog units– Managing a large number of concurrent users


Evaluation Criteria for Final Solution

• Scalability• Response time• Maximum number of concurrent users• Data storage and retrieval• Flexibility of geovisualisation options• Ease of use and simplicity• Intellectual Property Rights (IPR) issues• Cost of development and implementation


Geovisualisation Prototypes

• Different technologies have been explored:– WMS/ WFS– Adobe Flash (Flex) vector maps– SVG vector maps– KML vector maps with Google Maps API– Raster maps with OpenLayers


CensusGIV: Timeline

• October 2008 – February 2009– Evaluation phase (completed)

• October 2009 – June 2010– Developing prototype

• Trade-offs to be made between:– response time, storage space, concurrent users, IPR protection,

ease of navigability, flexible visualisation, back-end/front-end solutions, cost

• First version of prototype to be tested this month• ONS / Census Programme to decide full implementation

for 2011 Census


Design Considerations


Fundamental Design Decisions

• Server-based rasters– Faster on the client side– Fast enough on the server side– Not delivering restricted data to the client

• Open Source software– Leverage the powerful OpenLayers mapping API– More powerful than Google Maps API– An active development community– Full access to the source – can do “cool stuff”

• “Slippy” map– Intuitive– Encourages exploration


Maps of Population Data

• Cartograms– Fairer

representation– Multiple variables

can be shown together

• Choropleth Maps– Easier to relate to

• Surface mapping– Interpolation


Accessing the Census Data

• Neighbourhood Statistics– Hunter vs Gatherer– NeSS Data API (SOAP)– CSV Downloads

• Still tedious – for each UV:– Download files for each GOR– Stitch them together (has been automated)– Create corresponding tables in the database, add data– Add ranking scores– Add metadata

– NeSS Data API (REST) coming February 2010

• CASWEB


Structure of The Web-App

• OpenLayers “slippy” map– Fully opaque grey base layer

• Could be switched for aerial imagery from Google/Microsoft

– Opaque choropleth overlay• Variable translucency if aerial

imagery underneath

– Context overlay• Points, lines and names

• Sea area in lighter grey

• Otherwise transparent

– POIs• e.g. schools, hospitals

• SVG vectors rather than tiles

• “Clickable”


Screenshot


Why a Custom Context Layer?

• Having full control is a definite advantage– Underlay

• Google colours/features can clash with choropleths

• Lose the context if choropleth is fully opaque

– Overlay• Google labels can obscure information

• Google’s cartography recently changed (for the better)

• But no control over future changes


Cartography of the Context Layer

• Difficult to get right– Urban vs Rural

• Strictly Black & White• Few point features

– Hospitals, airports, place names

• Fewer areal features– Lakes, sea

• Mainly a network of roads/rivers/railways• Less is more


Creating the Context Layer

• PostGIS database– Using the OpenStreetMap dataset for the UK– Relatively slow to create the images from the data

• ~50 database queries for each image tile

• Higher zoom levels have tiles with smaller extent, but we include more detail at these levels, which cancels out the speed increase

– Render on demand “unimportant” tiles at zoom levels 16-18– Pre-render everything else

• Painter’s Algorithm


Painter’s Algorithm

• Two hierarchies of layering– Feature-based layering

• Land, water, road/railway casings & cores, place names

– Intra-feature level z-ordering• Complex road junctions• Railway/road crossing


Pre-rendering of Context Layer• Rendered on “gibin”, a quad-core computer running Linux

• Utilising the Python “Threading” module – 4 tiles created at once

• The image “tiles” are PNGs with an alpha layer

• Bounding box: -10.7 W to 1.8 E, 49.8 N to 60.9 N (All of the UK)

Zoom Level

Scale No of Tiles

Size /MB

Detail Time /min

6-9 < 1:1M 790 5 Cities, motorways < 1

10 1:600,000 2,146 15 + towns, trunk roads, lakes 1

11 1:300,000 8,208 40 + main roads, rivers, airfields 2

12 1:150,000 32,318 156 + minor roads, railways, villages 7

13 1:72,000 128,250 500 + main road, water & area names 24

14 1:36,000 510,962 1.4 GB + paths 1h 28

15 1:18,000 2,041,572 4.4 GB + minor road names 5h 34


The Context Layer (Levels 6-11)


The Context Layer (Levels 12-17)

On Demand On Demand


Creating the Choropleth Layers

• PostGIS database of census data• Would never want to pre-render all the choropleths at all zoom levels

– 1000+ metrics × 10 groupings × 30 colour schemes × 2 colour orders × 13 zoom levels × 000s of tiles per zoom

– Makes sense to cache most popular zoom levels, metrics, colours

– Most people will never “explore” the map at a greater zoom level - usage decreases exponentially with the number of clicks in a web app.

• Specially crafted URL– Boundary Table, Data Table, Metric

– Bounding Box, Zoom

– Colour Scheme, No of Groups

– Range Type, Range Attributes• Min/Max

• Average/Deviation


The Modifiable Areal Unit Problem

Boundary Type

Level Average No of Vertices (Simplified)

Number

MSOA 6, 7, 8 145 7,196 (Eng & Wal)

LSOA 9, 10, 11 62 40,884 (not N.I.)

OA 12 - 18 26 223,131



MSOA



LSOA



OA


Colour Theory

• “practical guidance to colour mixing and the visual impacts of specific colour combinations”

• Formal considerations– Colour harmony (complementary colours – pink vs blue)– Colour context (bright colours beside subdued colours)– Colour blindness

• Very subjective


Colour Considerations

• Colour should relate to data type:– Sequential– Diverging– Qualitative

• The “most of the UK is countryside” problem– Try not to use bright colours for the countryside.

• Hot Bad High Cold Natural Good Neutral Girls Boys


Colour Harmony

• Colour Harmony– Complementary Colours– Analogous Colours

• Colour Variation– Hue– Saturation– Lightness


Colourbrewer

• Cynthia Brewer’s colorbrewer2.com– Provides a set of “good” colour schemes which can be

incorporated easily into Python scripts, ArcMap, etc.– Generally vary by hue and/or lightness

• Sequential– Lightness should be varied, use analogous colours if varying hue– Plenty of “good” maps that don’t follow this rule

• Diverging– Mid-point should be a light colour– Extremes should have darker colours with complementary hues

• Qualitative– Hues should vary


Aerial Imagery

• Very easy!• OpenLayers

– Google Maps imagery layer– Microsoft Virtual Earth layer

• Only useful when zoomed in• Need to be mindful that

colour imagery interferes with choropleth colours

• No longer self-contained

layerAerial = new OpenLayers.Layer.Google("Aerial Imagery", {numZoomLevels: 16, type: G_SATELLITE_MAP, sphericalMercator: true});


Points of Interest (POIs)

• PostgreSQL (or MySQL) database• Can be a completely separate server• Client’s OpenLayers does the work• Aim is to provide even more context

• School names & performance indicators


User Interface

• How do you get people to explore the maps?– Maptube “visual directory”– Hierarchical drop-down lists– Tag cloud of keywords, maybe with a hierarchy

Less is more Choice is good


User Interface – Tag Cloud

• Useful for exploring if you don’t know what you want• More structured alternative needed for specific research


System Architecture


A Note on Python

• If you don’t use it already, you will!– ArcGIS 9.4

• “Python is now integrated directly into ArcMap [9.4]. I say it every year, but if you are an ArcGIS Desktop user, you need to take a close look at python as your scripting language.” - James Fee

• The best thing about Python is:– Tidy scripts!


Server Room

Servers

tiler1tiles1

tiler2tiles2www

tiler3tiles3

blog pois

Web browsers

tbadev


System Architecture – Website

Apache(www)

Web browser


System Architecture – Context

Apache(tiles2)

Tile exists

?

Yes

Tile

No

404 • No python involved– Less strain on the

server

• Web browser may have to request image twice– Slow for the client

Apache(www)

Web browser


Python

System Architecture – Context

Apache(tiler2)

renderer.py

Cache

XML

gen_tile.py

mod_python

Apache(www)

Web browser

XML


Python

System Architecture – Choropleth

Apache(www)

Apache(tiler3)

mod_python

renderer.py

Tile exists

?Yes

Tile

No

Cache (low

zoom)

Colorbrewer

gen_tile.py

Web browser


Scalability

• OpenLayers allows multiple servers to be specified for retrieving image tiles• Different servers for

different tasks• Random server chosen

per-tile• So should scale?

• Process is still processor intensive if generating the tiles at the same time

• Stress testing needed


Prototype: Current State & Next Steps

On-demand tile generation

Fast (enough) Will scale (hopefully!) OSM not quite “complete”

but getting there Context layer finished Some data added

× Legend× Automated data updates× Tag cloud× Improve cartography× Internet Explorer 6

www.savethedevelopers.org× Other census & ONS data× Interactive data combination× Scotland & Northern Ireland× Points of Interest

Running until June 2010


Live Demo

• http://www.censusprofiler.org/prototype/


Google Earth


Q&A

www.censusprofiler.org

www.oliverobrien.co.uk

Google Street View and Google Earth POI data is Copyright Google. Google Maps mapping data is Copyright Tele Atlas. Google aerial imagery is Copyright Digital Globe, Infoterra Ltd, Bluesky, GeoEye, Getmapping plc, The Geoinformation Group. OpenStreetMap data is CC-BY-SA OpenStreetMap and contributors. Logos depicted are generally Copyright of their respective organisations. Some image tiles include boundary information supplied by EDINA’s UKBORDERS service. The Census data is supplied by the Office for National Statistics.

The Word Cloud was produced with Wordle. The colour wheel diagrams are from worqx.com. The Painter’s Algorithm picture and the HSL colour diagram are from Wikipedia. The cartogram was produced by James Cheshire. The corresponding choropleth was produced by the BBC.

The following references were used in the first part of this presentation:CIBER (2008) information behaviour of the researcher of the future. A report commissioned by The British Library and JISC 11 January 2008. http://www.bl.uk/news/pdf/googlegen.pdf Goodchild (2007) Citizens as Sensors: The world of Volunteered Geography. Workshop on Volunteered Geographic Information, Santa Barbara, CA. December 13-14, 2007 http://www.ncgia.ucsb.edu/projects/vgi/docs/position/Goodchild_VGI2007.pdfO’Reilly, T (2005) What Is web 2.0 Design Patterns and Business Models for the Next Generation of Software http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/whatisWeb20.htmlTurner A (2007) Introduction to Neogeography. O’Reilly Media Short Cuts.

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