Navigating the transition to a sustainable world is humanity’s current challenge. This will require fostering resilient interacting systems of people and ecosystems (resilient social-ecological systems). In this course, students will utilize a suite of tools and ideas useful in managing ecosystem services in a sustainable way. Each week we focus on a different theme related to sustainability and resilience, and work as small teams to apply these ideas to a set of diverse landscapes throughout the world.

Many of the lab exercises and group projects involve “hands-on” spatial analysis of land cover change using GIS and remote sensing. We also assess socio-economic data to examine issues such as energy poverty. These quantitative approaches help us move beyond arm-waving about sustainability to making some tough choices, using the best available scientific information.

== SDG resources ==
* [https://www.globalgoals.org/ Sustainable Development Goals]
* [https://sdg-tracker.org/ Tracking SDGs]
* [https://en.wikipedia.org/wiki/Sustainable_Development_Goals SDGs on Wikipedia]

== Data collection ==

=== Understanding data ===
The group projects for CONS452 may rely on a variety of data types, depending on the problem context. Data may vary in their content, collection methods, scale, and formats.
* Primary vs. secondary data
* Qualitative vs. quantitative data
* Observational vs. experimental data
* Cross-sectional vs. longitudinal data
* Socio-economic vs. ecological data

{| class="wikitable"
|+
!Type of data
!Names of data sources
|-
|Socio-economic data
|[https://dhsprogram.com/data/Model-Datasets.cfm DHS] ([[Course:Cons452/DHS|Explainer]] , [[Sample data]])
[https://usa.ipums.org/usa/index.shtml IPUMS] ([[Course:Cons452/IPUMS|Explainer]], [[Sample Data]])

[http://surveys.worldbank.org/lsms LSMS] ([[Course:Cons452/LSMS|Explainer]] , [[Sample data]])

[https://sedac.ciesin.columbia.edu/data/collection/gpw-v4 Gridded Pop World] ([[Course:Cons452/GriddedPopWorld|Explainer]] , Sample data)

[https://mics.unicef.org/ MICS] ([[Course:Cons452/MICS|Explainer]] , Sample data)
|-
|Climate data
|[https://www.worldclim.org WorldClim] ([[Course:Cons452/WorldClim|Explainer]], Sample data)
|-
|Land use data
|[https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html Hansen Global Forest Change ] ([[Course:Cons452/Hansen|Explainer]] , [[Sample data]])
[https://www.protectedplanet.net/ World Database on Protected Areas] ([[Course:Cons452/WDPA|Explainer]] , Sample data)

AgChange (Explainer , Sample data)

[http://www.earthstat.org/ EarthStat] ([[Course:Cons452/EarthStat|Explainer]] , Sample data)
|-
|'''''Data search engines'''''
|[https://earthengine.google.com/ Google Earth Engine]

[https://energydata.info/ Energydata.info]

[https://freegisdata.rtwilson.com/ Free GIS Data]
|}

== Data analysis ==
For CONS452, we are encouraging the use of R for all data analysis (at least non GIS).

RStudio is pre-installed on all the desktop systems in the lab. For your personal computers, please download it [https://rstudio.com/products/rstudio/download/ here].

R can perform all functions available in MS Excel.

Detailed help/guidelines on R for this course are available [[Course:Cons452/UsingR|'''<big>here</big>''']].

== All Subpages ==
<dpl>
titlematch=Cons452/%
namespace=Course
shownamespace=false
</dpl>
[[Category:Assignments]]
[[Category:Labs]]

Course:CONS452

2019-11-07T23:32:31Z

VikasMenghwani: Replaced content with "{{Delete}}"

Course:Cons452

2019-10-07T18:56:34Z

VikasMenghwani:

{{Infobox_New_Course
|title= Global Perspectives Capstone
|course number= CONS452
|instructor= Dr. Jeanine Rhemtulla
|instructor 2= Dr. Hisham Zerriffi
|instructor 3= Dr. Terry Sunderland
|schedule=
|classroom=1001
}}Navigating the transition to a sustainable world is humanity’s current challenge. This will require fostering resilient interacting systems of people and ecosystems (resilient social-ecological systems). In this course, students will utilize a suite of tools and ideas useful in managing ecosystem services in a sustainable way. Each week we focus on a different theme related to sustainability and resilience, and work as small teams to apply these ideas to a set of diverse landscapes throughout the world.

Many of the lab exercises and group projects involve “hands-on” spatial analysis of land cover change using GIS and remote sensing. We also assess socio-economic data to examine issues such as energy poverty. These quantitative approaches help us move beyond arm-waving about sustainability to making some tough choices, using the best available scientific information.

== SDG resources ==
* [https://www.globalgoals.org/ Sustainable Development Goals]
* [https://sdg-tracker.org/ Tracking SDGs]

== Syllabus ==
Syllabus is present [https://wiki.ubc.ca/images/7/70/CONS452_Syllabus.pdf here]

== Class lectures ==

== Lab Assignments ==

{| class="wikitable"
!Week1
![https://canvas.ubc.ca/courses/18915/assignments/287860?module_item_id=984163 A1: Intro to qGIS]
|-
!Week2
![https://canvas.ubc.ca/courses/18915/assignments/287874 A2: Statistics refresher]
|-
!Week3
!
|-
!Week4
!
|}

== Data sources ==
{| class="wikitable"
|+
!Type of data
!Names of data sources
|-
|Socio-economic data
|[https://dhsprogram.com/data/Model-Datasets.cfm DHS] ([[Course:Cons452/DHS|Explainer]] , [[Sample data]])
[https://usa.ipums.org/usa/index.shtml IPUMS] ([[Course:Cons452/IPUMS|Explainer]], [[Sample Data]])
|-
|Natural resource data
|
|-
|Land use data
|[https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html Hansen Global Forest Change ] ([[Course:Cons452/Hansen|Explainer]] , [[Sample data]])
|-
|'''''Data search engines'''''
|
|}

== Statistical analysis using R ==
RStudio is pre-installed on all the desktop systems in the lab. For your personal computers, please down it [https://rstudio.com/products/rstudio/download/ here].

*[[Course:Cons452/UsingR|Getting Started]]
***

== All Subpages ==
<dpl>
titlematch=Cons452/%
namespace=Course
shownamespace=false
</dpl>
[[Category:Assignments]]
[[Category:Labs]]

Explainer

2019-10-07T18:47:21Z

VikasMenghwani:

{{Delete}}

'''Hansen Global Forest Change dataset''' 
Use the following credit when these data are displayed: 
Source: Hansen/UMD/Google/USGS/NASA 
Use the following credit when these data are cited: 
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.

'''Description''' 
The Hansen Global Forest Change dataset are results from a time-series analysis of Landsat (30m x 30m spatial resolution) images which characterize global forest extent and global forest change from 2000 to the most recent year (currently 2018). In this forest characterization dataset trees are defined as vegetation taller than 5m in height. The dataset was created and is maintained by Matthew Hansen (University of Maryland) and his team and is the most comprehensive dataset of forest cover and forest change available publicly.

For more information about the dataset please see the journal article: Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A. A., Tyukavina, A., ... & Kommareddy, A. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853.

Although the dataset is comprehensive, there are some disadvantages. For example, it does not distinguish tropical forests from plantations and herbaceous crops, leading to an underestimation of forest loss in tropical areas (https://science.sciencemag.org/content/344/6187/981.4). Review the literature and ensure that this dataset is a good fit for your project.

'''Metadata''' 
The following is taken directly from the https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html website (however the formatting of the text has been changes to fit this wiki), underneath the heading Dataset Details (Hansen 2013):

“This global dataset is divided into 10x10 degree tiles, consisting of seven files per tile. All files contain unsigned 8-bit values and have a spatial resolution of 1 arc-second per pixel, or approximately 30 meters per pixel at the equator.

Tree canopy cover for year 2000 (treecover2000) - Tree cover in the year 2000, defined as canopy closure for all vegetation taller than 5m in height. Encoded as a percentage per output grid cell, in the range 0–100. 
Global forest cover loss 2000–2014 (loss) - Forest loss during the period 2000–2014, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 1 (loss) or 0 (no loss). 
Global forest cover gain 2000–2012 (gain) - Forest gain during the period 2000–2012, defined as the inverse of loss, or a non-forest to forest change entirely within the study period. Encoded as either 1 (gain) or 0 (no gain). 
Year of gross forest cover loss event (lossyear) - A disaggregation of total forest loss to annual time scales. Encoded as either 0 (no loss) or else a value in the range 1–14, representing loss detected primarily in the year 2001–2014, respectively. 
Data mask (datamask) - Three values representing areas of no data (0), mapped land surface (1), and permanent water bodies (2). 
Circa year 2000 Landsat 7 cloud-free image composite (first) - Reference multispectral imagery from the first available year, typically 2000. If no cloud-free observations were available for year 2000, imagery was taken from the closest year with cloud-free data, within the range 1999–2012. 
Circa year 2014 Landsat cloud-free image composite (last) - Reference multispectral imagery from the last available year, typically 2014. If no cloud-free observations were available for year 2014, imagery was taken from the closest year with cloud-free data, within the range 2010–2012. 
Reference composite imagery are median observations from a set of quality assessed growing season observations in four spectral bands, specifically Landsat bands 3, 4, 5, and 7. Normalized top-of-atmosphere (TOA) reflectance values (ρ) have been scaled to an 8-bit data range using a scale factor (g):
DN = ρ · g + 1 
The g factor was chosen independently for each band to preserve the band-specific dynamic range, as shown in the following table:” 
Landsat Band g 
Band 3 (red) 508 
Band 4 (NIR) 254 
Band 5 (SWIR) 363 
Band 7 (SWIR) 423 
 
Tiles over the ocean are provided for completeness and do not contain meaningful data.

'''Downloading Instructions''' 
Go to the url:
https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html
Then scroll down to the subheading “Download Instructions”. You have to download them in tiles that are 10 degree cubes, and so you must click on your desired cube(s) on the site. You’ll notice that the links below the map will change to match up with the tile you have just clicked on. Each link corresponds to a layer file (.tif) for that tile (one of the 7 layer files aforementioned), and you can download select layers or all of them for the same tile.

'''Restriction on Use''' 
Creative Commons Attribution 4.0 International License 
Please see https://creativecommons.org/licenses/by/4.0/legalcode for full license terms but this essentially means that you are free to use, share, and adapt this data as long as you attribute it and indicate how you have manipulated or built upon the data (if you do so).
Sample* i believe due to licensing that I can have a sample here without issue for this specific dataset.

'''Sample'''

of the attribute table for a spatial dataset or a screenshot?