Mapping population 24/7: direction of David Martin (Samatha Cockings, Samuel · 2015-09-07 · Mapping population 24/7: direction of travel David Martin (Samatha Cockings, Samuel

Mapping population 24/7: direction of travel David Martin (Samatha Cockings, Samuel

Leung, Alan Smith)

Transportation Research Group Seminar 28 November 2011

2

Presentation overview

• Small area spatial population distributions

• The time dimension

• Data considerations

• Modelling and visualizing population 24/7

Small area spatial population

distributions

100+ years of census mapping

Charles Booth: poverty in Pimlico 1898-9

Neighbourhood Statistics: households lacking amenities 2001

Sou

rce:

nei

ghbou

rhoo

d.s

tati

stic

s.go

v.uk

Source: booth.lse.ac.uk

Small area spatial population distributions • Resource allocation: large areas > small areas

• Targeting services/marketing

• Site location decisions/transportation demand

• Denominator populations

• BUT limitations derived by traditional representational concepts and data sources: irregular geographical areas and the missing time dimension

6

Deficiencies of the census map

• Long-term reliance on shaded area mapping to inform spatial decision-making

• Most commonly dependent on traditional census-type data collection - underenumeration, delay to publication, infrequency, change over time, administrative factors determine zones

• Modifiable areal unit problem, representational problems

• Focus on residential base, “night-time” populations

• All these factors continue to shape most mainstream GIS applications that handle population data

8 Photos: David Martin, Sam Cockings

The time dimension

10

Need for better time-space distributions

• Conventional population mapping – whether area-based or gridded – focused on residential “night-time” populations

• Widespread demand for population maps which are more temporally appropriate, in two ways:

– Up to date (chronological time)

– Relating to a relevant time period (cyclical time)

• Especially where population “exposure” is concerned: emergency planning, exposure to risk, services to dynamic populations, etc.

Observations…

• There have been enormous advances in geo-visualization techniques, computing power and dynamic modelling sophistication

• Most of the population spends much of the time away from home, engaged in a wide variety of non-residential activities

• We have not yet seriously tackled the entire area of space-time-specific population modelling

Photos: David Martin

Southampton 2001 OAs (730)

>625 Workers (Split to form WPZ)

100 - 625 Workers (Acceptable as WPZ)

<100 Workers (Merge to form WPZ)

15 Photos: David Martin

16 Photos: David Martin

Some progress in

capturing

residential

development,

second homes...

Space-time population modelling

• Where tried, the general approach is to start with night-time population model/map and transfer population subgroups to specific daytime locations, e.g. schools, workplaces

• Various recent application examples, particularly driven by emergency planning and modelling of population exposure to hazards

• In reality, many different timescales to be modelled, not just simple ‘daytime’ and ‘night-time’

• Longstanding difficulty of obtaining data with sufficient space/time resolution for the non-residential addresses

18

http://w

ww

.ornl.gov/sci/gist/landscan/landscanU

SA

/landscanU

SA

_factsheet_O

RN

L.pdf

19

Herzog and H

ofstetter, 3

D visualization of

day tim

e popuation, Zurich

Next steps

• Already have a method for building gridded population models (more later)

• We want to be able to build time-specific gridded population models

• This requires constructing conceptual and practical models for time-specific population activities

• These need to be operationalised using existing, relevant data

• Remember our starting point is mapping, not trip modelling or microsimulation

20

21

Photos:

David Martin

Data considerations

23

Ho

me R

esid

ence

Offic

e W

ork

Outd

oors

Work

All E

mp

loym

ent

Oth

er W

ork

Ed

ucation b

y S

tage

All E

ducation

Oth

ers

Ro

ad

s

Tra

nsport

Hubs

0%

20%

40%

60%

80%

100%

00:00

02:00

04:00

06:00

08:00

10:00

12:00

14:00

16:00

18:00

20:0022:0000:00

Po

pu

lati

on

D

istr

ibu

tio

n

(%)

Time(Hour)

• Conventional population map interpreted over time

Total

population

+/-

external

visitors

Non-

residential

Transport

Residential

Total

population

+/-

external

visitors

Private dwellings

Non-

residential

Communal ests.

Transport

Employment

Education

Residential

Temp accomm.

Generalised local

Family/social

Retail

Leisure

Tourism

Healthcare

Rail

Metro/subway

Air

Water

Road

Locations

Acronyms: QLFS Quarterly Labour Force; DCSF Department for Children, Schools and Families; HESA Higher Education Statistics Agency;

Survey; DCMS Department for Culture, Media and Sport; ALVA Association for Leading Visitor Attractions; DfT Department for

Transport; TfL Transport for London; CAA Civil Aviation Authority

Total

population

+/-

external

visitors

Private dwellings

Non-

residential

Communal ests.

Transport

Employment

Education

Residential

Temp accomm.

Generalised local

Family/social

Retail

Leisure

Tourism

Healthcare

Rail

Metro/subway

Air

Water

Road

Locations Data Sources

- Census, Mid-Year Population Estimates (MYEs)


- Census, Annual Business Inquiry, QLFS

- EduBase, DCSF school performance tables, HESA

- VisitBritain, Annual Business Inquiry

- VisitBritain

- Annual Business Inquiry, commercial sources

- ALVA Visitor Statistics, DCMS


- Hospital Episode Statistics

- National Rail station usage data

- DfT Light Rail Statistics, TfL Tube customer metrics

- CAA UK Airport Statistics

- DfT Sea Passenger Statistics , London River Services

- DfT Road Statistics, Annual Average Daily Flow

-




Total

population

+/-

external

visitors

Private dwellings

Non-

residential

Communal ests.

Transport

Employment

Education

Residential

Temp accomm.

Generalised local

Family/social

Retail

Leisure

Tourism

Healthcare

Rail

Metro/subway

Air

Water

Road







- VisitBritain










-




Total

population

+/-

external

visitors

Private dwellings

Non-

residential

Communal ests.

Transport

Employment

Education

Residential

Temp accomm.

Generalised local

Family/social

Retail

Leisure

Tourism

Healthcare

Rail

Metro/subway

Air

Water

Road







- VisitBritain










-

29

http://cwswg.wikidot.com/

http://w

ww

.neighbourhood.statistics.gov.

uk/

http://data.gov.uk/

30

Transport

• Rasterised road background layer Meridian™ 2 v1.1 Release 2 2010

– Motorway (blue)

– Trunk A-Road (green)

– Principal A-Road (grey)

• DfT NTM Area Type in the study area:

– Rural (green)

– Urban (peach)

• AADF Count Points (2006)

33

00:00

02:00

04:0006:00

08:0010:00

12:0014:00

16:0018:00

20:0022:0000:00

Hom

e R

esid

ence

Offic

e W

ork

Outd

oors

Work

Reta

il W

ork

Oth

er

Work

School E

ducatio

n

Hig

her

Educatio

n

Oth

ers

Roads

Tra

nsport

Hubs

0%

20%

40%

60%

80%

100%

Po

pu

lati

on

Dis

trib

uti

on

(%

) .

Time

(Hour)

• Integrated multi-source datasets interpreted over time

Modelling and visualizing

population 24/7

35

Spatial modelling framework

• Builds on existing grid modelling methodology developed for use with conventional census data

• One of a variety of approaches to reallocation of population counts from one set of geographical features to another

• Uses adaptive kernel estimation to generate gridded population estimates from input points (‘centroids’)

• A key advantage of gridded models is continuity of spatial units through time (i.e. no boundary changes)

36

Centroids, boundaries and grids

Centroid locations and boundaries Centroid populations redistributed onto grid

Centroid set

• Gridded surface (from postcodes)

Study area a at time t

study area a

time

t

Background layer for time t

study area a

time

t

background layer b

Background layer: land use/transportation

• This mostly refers to the transportation network, which contains variable and sometimes very high populations, mostly time-dependent

• Also identifies very low-density areas (open water, mountains, cornfields) whose population is effectively zero at all times

• We have used road network and traffic flows, by area type, as defined by Department for Transport’s National Transport Model, combined with OS Opendata Meridian network

40

Adjust for visitors

study area a

time

t

background layer b

- visitors out + visitors in

Destination centroid i at time t

study area a

area of influence j

local extent d

centroid i

background layer b

time

t

Origin centroid within area of influence j

study area a

area of influence j

local extent d

centroid i

background layer b

time

t

Time-space data handling

• Requires extensive library of centroid locations

• Scope of input centroids defines the scope of output model

– Conventional residential locations with population totals (e.g. postcodes, census output areas)

– All other locations, with population capacities, time profiles and areas of influence (e.g. schools, hospitals, workplaces)

• Population further subdivided into sub-groups e.g. by age

44

Time profiles

• Variety of sources, but only need reasonable reference time profiles for each type of activity – more detail can be added for specific sites or further subdivision of activity later

• Opening hours by various services readily obtainable (schools, etc.)

• Quarterly Labour Force Survey for workforce time profiles (daytime, evening, night working, hours worked, days worked by SIC categories)

45

Time profile example – school

46

00 06 12 18 00 Time of day

Population

In transit

Present

Input data points

• Space-time centroid: hidden text

– Population capacity

– Spatial extent

– Time profile

– Area of influence

• e.g. primary school, output area centroid

– Pupil numbers

– Small (one cell)

– Term dates, school day

– Catchment area (modelled time/space)

47

Mapping activities to time and space

48

Census/

Mid-Year

Estimates

Georeferencing:

Time profiling:

Special populations:

Areal population

counts to be

distributed to

postcode level in

proportion to

number of postal

delivery points

Numbers of

employees of

larger and small

business

enterprises based

on typical

employers

Pupil numbers at

schools by stage

of study

Student numbers

in further and

higher education

to be linked with

census records of

working age

people in full-time

education

Residential

postcodes then

grid coordinates

Business and

residential (for

home-based

workers)

postcodes then

grid coordinates

Nursery, primary

school,

secondary

school, college

and university

postcodes

Household Time

Use Survey

Quarterly Labour

Force Survey

Academic

calendars,

timetable, school

and campus

opening hours From census

identify

immovable

communal

residents e.g. in

long term health

care, prisons

Census Travel to

Work data

From census,

identify students

living away from

home

Background layer b

when time = ti

Higher

education

statistics

EduBase

school

records

Annual

Business

Inquiry

49

50

02:00

Residential “night-time” model; considerable goods vehicle traffic on motorway & trunk roads

Southampton, 200m cells

08:00

51

Early workplaces, docks, industrial estates; rest as residential; near-peak traffic


09:00

52

Workplaces, educational institutions, “daytime” model; low residential; very high central densities; peak traffic volume


16:00

53

Workplaces, FE & HE institutions still open, schools closed; low residential; very high central densities


18:00

54

Late workplaces remain, education closed; return to residential; high central densities


55

21:00

Residential “night-time” model; late night light traffic flow on all roads


56

57

58

59

60

61

62

Current state of project

• Conceptual model established

• Collection of representative input datasets completed for purposes of initial funded project – incomplete coverage

• New variations possible post-2011 census results

• Complete working version of SurfaceBuilder247 software

• Demonstration website being prepared at Manchester

• Formal published outputs in preparation

• Talking to variety of potential users

63

Future directions 1

• Generalisable modelling framework for spatio-temporal population representation

– e.g. different spatial interaction models, transportation models

• Validation challenges

– Interest in mobile telephony and business data

• Computational refinements required

• Data from web services: near real-time update and modelling?

Future directions 2

• Alan Smith PhD (ESRC DTC) – examining implications of spatio-temporal population model for assessing population exposure to environmental hazard

• New potential from 2011 census data

• Interest from HR Wallingford, HSE, HPA

• What about the transportation implications??

• Further funded projects/other PhD topics??

66

Acknowledgements

• Economic and Social Research Council award number RES-062-23-1811

• Employee data from the Annual Business Inquiry Service, National Online Manpower Information Service, licence NTC/ABI07-P3020. Office for National Statistics 2001 Census: Standard Area Statistics (England and Wales): ESRC Census Programme, Census Dissemination Unit, Mimas (University of Manchester). National Statistics Postcode Directory Data: Office for National Statistics, Postcode Directories: ESRC Census Programme, Census Geography Data Unit (UKBORDERS), EDINA (University of Edinburgh). Quarterly Labour Force Survey, Economic and Social Data Service, usage number 40023. Mastermap ITN layer: © Crown Copyright/database right 2009, an Ordnance Survey/EDINA supplied service.

Questions, discussion.

Documents

Mapping population 24/7: direction of David Martin (Samatha Cockings, Samuel · 2015-09-07 · Mapping population 24/7: direction of travel David Martin (Samatha Cockings, Samuel