This document summarizes the development of a land use model for a developing country. It describes the challenges faced due to lack of data, the approach taken to address gaps, and lessons learned. Key challenges included limited road, traffic, and land use data. The approach involved defining demographic data sets, mapping existing and future land use patterns based on growth assumptions, and processing data to incorporate it into the model. Outcomes showed modelled versus observed data matched reasonably well. Lessons learned emphasized using local knowledge, being pragmatic given data limitations, and patience.

1. Land Use Modelling for
Developing Countries
Presenter: David Freer
Thursday 14 August 2014

2. • The Project
• Developing the model – challenges
• Approach adopted – step through
• Outcomes and lessons learnt
Note: Project is live and commercial in confidence – no actual
model data can be provided
Presentation Overview

3. The Project

4. Building the Model – data inputs,
parameters, outcomes

5. The Study Area

6. Model Development – Challenges
• Road Network
• No consistent or accurate road database (GIS centre line, lanes, speeds etc)
• Limited future network planning – only ADB projects
• Road Operations
• No reliable traffic counts
• No OD or TT Surveys
• No freight use surveys
• Model parameters
• Nothing official – we developed them
• Land Use
• Limited census data – very limited place of work data
• No HTS, JTW – very limited travel behaviour data only observed
• Limited car registration

7. Future Year Land Use Planning

8. • Now what?
Gap Analysis

9. • What have we got and how we can get more
• census and other data analysis and manipulation
• Survey options/consultation
• Get help- uni
• Zoning system - Divisional Secretary’s Divisions (DSD)
• Assessment Time Frames – 20 year project assessment
• Mapping land use patterns
• Assumptions and guiding principles
• Getting to a base data set
• Data processing
• Running the model and scenarios
Making sense of it all – Land use

10. • GIS land use mapping
• Get all the data – its out there somewhere
• Demographic Data sets – we define
• Population groups
• Household
• Vehicle registrations
• Employment groups
• Assumptions
• live and work in relatively close proximity – DSD total generally as
control
• Generic industry destines per employment type were used as a guide.
• 2001 census data land use patterns were largely reflective of 2012 land
patterns
Mapping the land use - existing

11. Percentage Growth per year
2011 - 2016 2016 - 2021 2021 - 2023 2026 - 2036
Employment 1.79% 1.65% 1.44% 1.24%
Population 1.41% 1.26% 1.12% 0.99%
Household 1.93% 1.85% 1.78% 1.46%
Motor Car Registration 7.28% 6.61% 5.96% 5.43%
Mapping the land use – future
• Growth rates outside of growth centres were based on historical
growth, national growth projections with following assumptions
• Annual growth rates will progressively slowdown in the long term
future
• Average household size will gradually reduce in the future
• Vehicles per household will gradually increase in the future
• Employment: population rate will gradually increase in the future
• Total population and employment growth capped to projected growth
rate for that date period for whole study area

12. Data Processing

13. • Data Cleansing and Analysis
• Base Year (2012) Demographic
Data
• Population
• Employment
• Registration
• Future Year Growth Rates –
balancing
• Incorporation into the model
• Trip generation rates
(regression analysis)
Data Processing

14. Population Growth – Study Area

15. Employment Growth – Study Area

16. Modelled Versus Observed
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
0 10,000 20,000 30,000 40,000
Modelled
Observed

17. • On the ground time and
discussions
• Use local knowledge and support
• Getting the right team
• Be pragmatic and flexible –
preparation, project framing,
assumptions, technology V
experience
• Patience and persistence
Key Lessons Learnt

18. Thank You
Banora Point Upgrade, New South Wales, Australia