Spatial datasets in support of decision making

transcript

StGroup Conference – 15 September 2015

Overview• Introduction to GEOTERRAIMAGE (Pty) Ltd• Analysis and reporting requirements• African and South African datasets

• Spatial data products• Spatial data services

• Summary

GEOTERRAIMAGE (Pty) Ltd• Established in 1999• Clear focus on information provision:

– Remote sensing / Image interpretation– Field work– GIS Data fusion / Spatial modeling

• Other GISc services– GISc Services & consultancy

• Over 200 years of experience

GEOTERRAIMAGE (Pty) LtdProducts / Services

GEOTERRAIMAGE (Pty) LtdData Clients and Projects

• Vodacom• MTN• Cell-C• Nokia Siemens Networks• Telkom / Telkom Mobile• Telco Network Operators and

Service Providers across Africa• ESKOM

• AskAfrica• Aqynt• AfricaScope

• Peace Parks Foundation• African Parks Network

• Gauteng City Region Observatory• Statistics South Africa• Human Science Research Council• CSIR• CRUISE (Stellenbosch University)• Housing Development Agency

• Standard Bank• ABSA Bank

• City of Johannesburg• City of Tshwane

• TomTom • Nokia / HERE.com

GEOTERRAIMAGE (Pty) LtdPartnerships

• Spatial Technologies• TomTom South Africa• AfricaScope• SiQ• MetroGIS• Geo Data Design• Airbus (EADS)

Analysis and Reporting

Requirements

Unleashing the power of imagery, improving your business intelligence

Analysis and Reporting• Highlight where growth has occurred

• Where has densification occurred?

• Where has land use changed?Present

Future

• Indicate Day-time versus night-time population

• Where is present and future demand?

• What is the potential for change in a given area?

Present

Future

• Is there any indication of planned developments in an area?

• What is the demographic composition of an area?

• Where have landscapes or areas been transformed?

Present

Future

Southern African Land Cover7 land cover classes

No Class Area (km²) Percentage1. Water 14,949.6 1.9%

2. Bare earth 957.1 0.1%

3. Low vegetation (Grassland) 153,009.7 19.1%

4. Tree / Bush dominated areas 386724.8 48.1%

5. Tree dominated areas 246,116.4 30.6%

6. Cultivated (Commercial) 1,419.3

Sudan Land Cover7 land cover classes

African settlementsUrban / Village / Built-up

No Class Area (km²)1. Urban / Villages 8,614.8

2. Central Business Districts 1.5

3. Industrial 146.9

Updating African settlementsAverage visible light

Updating African settlementsStable visible light

African population distribution

No Class Area (km²) Population1. Concentrated 1,193.3 16,562,536

2. Scattered 211,295.4 20,196,760

3. Water 52,621.2 0

African clutter datasets (40m)

African cities clutter datasets (10m)

African cities datasets• There is limited detailed information available• The existing data is costly and may be of

limited value• We are working towards identifying the

relevant information requirements for major cities in Africa

• Dataset need to be sustainable and consistent across the continent

Area based land useGauteng (ex Smallholdings) – 1990/2013

Land-use 1990 (km²) 2013 (km²) GrowthBuilt-up 180.2 213.2 18.3%Commercial 120.8 144.5 19.6%Industrial 129.3 153.7 18.9%Informal 83.1 222.2 167.4%Residential 848.3 1022.0 20.5%Townships 165.8 361.9 118.3%

Area based land useThulamela Local Municipality – 1990/2013

National land cover dataset72 land cover classes

Land cover change detection Emalahleni (1990/2013)

Urban land cover (2010)

Urban land cover (2014)

New Developments (2013)

New Developments (2014)

Building based land use (2012)

Residential1,014,320

Non-residential86,365

Building based land use (2012)

Informal157,674

Backyard294,116

Building based land useResidential analysis (2001/2012)

Gauteng Residential Land Uses Formal Informal Backyard

20011,195,031 558,927 266,584

59.1% 27.7% 13.2%1,195,031 825,511

59.1% 40.9%

20121,604,249 593,158 816,283

53.2% 19.7% 27.1%1,604,249 1,409,441

53.2% 46.8%

StGroup Conference – 15 September 201538 2000 2009

Diepsloot densification

Building based land useResidential versus Non-Residential (2001/2012)

Super EA Summary Dataset• Provide socio-economic, demographic and

activity indicators– Integrating diverse datasets

• Consistent and easy to use reporting environment – Using the StatsSA 2011 EAs– All data standardised and

reported at this level

Historical Growth

FutureGrowth

Population

Demographics

Lifestyle

Residential

Land Use

Building Footprints

Landscape

Super EA Based IndicatorsNo Class Value1. Total Population 539

2. Total Households 233

3. Daytime population 500 to 1,000

4. Dominant LSM 10

5. Dominant Age class 30 to 34

6. Dominant Race Black

7. Dominant Gender Male

8. Primary Land Use Residential

9. Secondary Land Use Commercial

No Class Value10. Size of Commercial

buildings10,500m³

to 31,800m³

11. Major change has occurred since 2001

12. Change has been in residential land use

13. Chance of change in the future

Building Based Land Use (National)

No Class % of Residential1. Informal 12.0%

2. Formal Residential 18.2%

3. Township 17.7%

4. Villages 52.1%

Population distribution dataset

Summary

Unleashing the power of imagery, improving your business intelligence

Summary• Imagery is under utilised as an additional

source of information• We are able to quantify historical land use

change• We are able to assist in forecasting change in

the future• Datasets can be developed for areas where

imagery is available

Summary• We are striving to provide South African

equivalent data for Africa • We are working to understand the economies

of scale and the role-players in Africa• Working closely with existing and new

business partners to provide a complete solution