Amin tayyebi: Big Data and Land Use Change Science

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University of Tehran, Iran

Survey and Geomatic Engineer (GIS, Remote Sensing,

Photogrammetry and Geodesy)

University of Tehran, Iran

GIS

Purdue University, USA

Department of Forestry and Natural Resource

University of Wisconsin-Madison, USA

Wisconsin Energy Institute

Landscape Ecology

University of California-Riverside, USA

Center for Conservation Biology

Department of Botany and Plant Sciences

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Introduction

Land cover and land use

History of land change science

Sustainability

Big data and land use change science

Software development -> LTM-HPC

Summary of other projects

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Introduction



Sustainability




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One third to one-half (Turner, 1995)

Land use cover change (Foley et al., 2005)

Land change science

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Moving to urban areas

In 1900 (<10%)

In 2050 (>50%)

Occurred on <3%

Doubles every 30 years

Approach 10% by 2070

78% of carbon emissions

60% of water use

…

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Conversion of forest to agriculture in the Amazon

Local temperature

Carbon dioxide

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Fragmentation of natural habitats

Richness and abundance

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Earth as a system

Sustainability

Current and future needs

Land change science (Turner, 2007)

Future and historical land use map

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Introduction



Sustainability




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Using Big Data to Simulate Land Use Change at a

National Scale: An Application of Land

Transformation Model-High Performance

Computing (LTM-HPC)

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Managing the nation’s fish habitat at multiple spatial

and temporal scales in a rapidly changing climate

Land use

Climate change

Fish habitat

Research team

Scientists from the USGS, University of Missouri,

Michigan State University, and Purdue University

Series of meeting (3 years)

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Limitations

Discrete time periods

Particular regions

Coarse spatial resolution

Multiple land uses

Understand global process

Forecasting annual multiple land use changes at

continental scale -> 2000 to 2100

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Modeling land use change

Big data (GIS and remote sensing)

Data mining (Artificial neural network)

Calibration

Validation

Forecasting

Products and applications

Software

Programming (Python, C#, C++ and batch)

Communication (XML)

Parallel processing (High performance computing)

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Big data (Python)

Create pattern file (C# executable)

Data mining

Artificial neural network (C++ executable)

Calibration

Land use change quantity (C# executable)

Suitability map (C# executable)

Simulated map (C# executable)

Validation (C# executable)

Forecasting (C# executable)

Products and applications 17

Drivers in time 1 Land use change

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Workstation

Quantity of files

Size of files

Server

Parallel processing

…

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Drivers in 1992 Land use map Results

Distance to road NLCD 1992 Pattern file

Distance to urban NLCD 2001 Suitability map

Distance to stream Change map Simulated map

Distance to city center ---- Error map

Distance to highway ---- ----

Slope ---- ----

Gross domestic product ---- ----

Exclusionary zone (existing urban,

water, state parks and others)

----

----

20722 × 11 = 227942 ~ 228K

20722 × 4 = 82888 ~ 83K

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Slope_16_003.asc 22

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20722 × 11 = 227942 ~ 228K

20722 × 4 = 82888 ~ 83K

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Big data (Python)


Data mining


Calibration







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10000.net file

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Big data (Python)


Data mining


Calibration







Quantity of change between times 1 and 2

Simulated map in time 2

Sort suitability values

Reference map (time 2)

Status 1 (Non-Urban) Status 2 (Urban)

Reference map (time 1) Status 1 (Non-Urban) A B

Status 2 (Urban) C D

Drivers in time 1 Suitability Map

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B

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20722 × 11 = 227942 ~ 228K

20722 × 4 = 82888 ~ 83K

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Big data (Python)


Data mining


Calibration







Reference map (time 2)

Status 1 (Non-Urban) Status (Urban)

Simulated map (time 2) Status 1 (Non-Urban) True Negative (TN) False Negative (FN)

Status 2 (Urban) False Positive (FP) True Positive (TP)

Future Scenario

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20722 × 11 = 227942 ~ 228K

20722 × 4 = 82888 ~ 83K

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Big data (Python)


Data mining


Calibration







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20722 × 11 = 227942 ~ 228K

20722 × 4 = 82888 ~ 83K

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Big data (Python)


Data mining


Calibration







In Great Lakes area, LaBeau et al., (2014), used the

future land use maps (between 2010-2050)

Land use (agriculture and urban) and phosphorus

delivery

Increase P loadings by 3.5–9.5%

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Developing a model to simulate land use change at

continental scale

LTM-HPC

Sustainability

Climate, water quality and biodiversity

Big data and land change science

Land use legacy

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Tayyebi, A., Pekin, B. K., Pijanowski, B. C., Plourde, J. D., Doucette, J. S.,

and D. Braun. (2013). Hierarchical modeling of urban growth across the

conterminous USA: Developing meso-scale quantity drivers for the Land

Transformation Model. Journal of Land Use Science, 8(4), 422-442.

Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., and J.

Plourde. (2014). A big data urban growth simulation at a national scale:

Configuring the GIS and neural network based Land Transformation Model

to run in a High Performance Computing environment. Environmental

Modelling & Software, 51, 250-268.

Tayyebi, A., Pekin, B. K., and B. C. Pijanowski. (In review). Urbanization

trends across the conterminous of USA from 1900 to 2100: Lessons learned

from studies in 11 mega-regions. Regional Environmental Change.

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Advisor -> Bryan C Pijanowski

Post Doc -> Burak K Pekin

GIS Specialist -> Jarrod Doucette

GIS Specialist -> James Plourde

IT Specialist -> David Braun

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Introduction



Sustainability




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SmartScape™: A web-based decision support

system for strategic agricultural land use

policy development

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Urban Heat Island Variation across a Dramatic

Coastal to Desert Climate Gradient: An

Application to Los Angeles, CA Metropolitan Area

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Video time

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Mike Batty

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