Rubric for the cartogram project

Hi

You can download the rubric for your cartogram project below.

Download
ESS132 cartogram project rubric

Cheers
Martin

Lab times final week

Hi

The lab with ArcMap 10 is available this week on Tuesday 6 and Friday 9 November. It is closed for exams  on Wednesday 7 and Thursday 8 November.

Given these restrictions we will still accept your final submission on Monday 12 November.

I will be available for help on Tuesday and Friday this week.

Cheers
Martin

Submitting your cartogram project

Hi

If you have your four cartograms ready you can work on the power point presentation of your project. The details are in the document below which describes how to prepare each slide of your presentation and how to submit your project.

Please read the instruction carefully!!!!!
1. Don' t forget to add your student number everywhere that is indicated.
2. You will be submitting five png image files named with your student number - see document below.which explains how to do this.

FINAL DATE FOR SUBMISSION  9 November 2012

Downloads
Preparing and submitting your cartogram project presentation

Martin

Making your four cartograms and saving them to an image

Hi

Today you will make the four cartograms one for each question you asked and save them to jpeg images which can be included into your Power Point presentation.

Downloads
Save a map (cartogram) to an image (e.g. jpeg)

Have fun
Martin

Making cartograms from your data

Hi

This week we are going to finally make the cartograms using your worldmapper_all layer which is your to your reorganized excel spreadsheet. In the tutorial it explains that we first have to save the join into a new shape file and how to do this.

After this is done you can continue making the cartograms. Please refer to the last blog post for the method used to make the cartograms.

Download
Exporting the joined worldmapper_alllayer to a new combined shape file

Martin

Installing the Cartogram tool for ArcGIS

Hi

Some of you may be ready to make cartograms from your worldmap_all layer.

BUT before you can start with this step you must have done the following 

1. Reorganised your spreadsheets so that all your data in available in one spreadsheet
2. Removed any repeated columns from your spreadsheet
3. Changed the column heading to 8 characters without spaces, brackets etc (remember to make a note of what your new headings mean!)
4. Join your spreadsheet to your worldmapper_all layer
5. Added a column or columns classifying the countries of the world into categories  (These will depend on  the question which you asked)

If you have completed all this you can start making the cartograms.

REMEMBER TO SAVE ALL THE YOUR WORK IN A SINGLE FOLDER AND EMAIL IT TO YOUR SELF!

Adding and using the ArcGIS Cartogram tool

We are going to try to do this today but the permissions on the lab computer may prevent us from getting this far. In which case we have have the script installed on the machine by next week and I will give you another task in the class.

In order to make the cartograms we have to add a special script to ArcGIS.  This script come with full instructions and its own data follow the steps below!

1. Download the cartogram tool script readme file (below) and read it carefully before doing anything else!  I know I don't I just click and get frustrated!!!!
2. Install the script to a folder which you create on the desktop e.g. desktop/ArcScripts.
3. Don't get confused you are here to learn!
4. The script installs its own demonstration .mxd file with its own layers and the tool already added to the toolbox. This is available in the cartogram .mxd file which is in the directory where you installed the script e.g. desktop/ArcScripts. You can use this demo to practice making a cartogram by following the steps in the readme file.
5. Once you have practiced making a cartogram using the demo data in the cartogram.mxd you can try with the worldmap_all layer using the data you joined to it from your spreadsheet in your .mxd file. 
6. If you read the instructions carefully - you will note the following when making a cartogrm using your data!

• You must add the tool to the toolbox - follow the steps in the readme file.
• Make sure there are no negative values in the value field you are using - this will be one of the columns you added with the join. You can do this by adding the definition query - all explained in the readme.
• DON'T PANIC!
• Check the box "Copy the Original Polygon Features table to the Output Cartogram Features table" or your cartogram will loose much of its attribute data such as country name which is no good.

Downloads

Cartogram tool readme file

Cartogram tool script

Remember not to panic and have fun!

Martin

Organising your GIS data

Today you are going to:

1. Find data to divided your countries in the categories you have used in your research questions. 
2. Neaten up your spreadsheet
3. Join your spreadsheet to the world mapper layer in ArcGIS 10 
4. Learn about labels and symbology on you map

Downloads

Tutorial join a spreadsheet to GIS layer

An example workbook of reorganized data 

Have fun and enjoy

Introduction to a GIS

Hi

Today you are going to use ArcGIS for this you need the instruction which I have written out here

Tutorial on making your first map

The world Map GIS file you need can be found at

http://planet.uwc.ac.za/nisl/ESS/ESS132/GIS/worldmapper_all.zip

Have fun
Martin

ESS132 Globalisation Project

You need to identify one project theme, develop research questions built around a minimum of four data sets.  There are more than 400 suitable data sets to select from.  Each data set will be processed to answer a research question.  Data sets for your project can be found in Excel format from

http://planet.uwc.ac.za/nisl/ESS/ESS132/Worldmapper_data/

Themes and dataset suits or suggestions are provide - please select one - check its availability and book using comments.

A. History

1. Population Trends (1, 1500, 1990, 1960)

B. Movement

1. Immigration, Emigrations, Net Immigration, Net Emigrations
2. Tourism
3. International Refugees

C. Transport

1. Air Transportation
2. Road Transportation
3. Shipping Transportation

D. Food

1. Fruit and Vegetable Imports and Exports
2. Meat and Fish Imports and Exports
3. Dairy and cereal   Imports and Exports
4. Groceries, tobacco, wine and spirits Imports and Exports

E. Goods

1. Raw Materials: metals, steel and ores Imports and Exports
2. Petroleum products:   Imports and Exports
3. Energy related products:   Imports and Exports

F. Manufacturing

1. Cars and Vehicles:  Imports and Exports
2. Computers and Electronics:  Imports and Exports
3. Chemicals and Natural Products:  Imports and Exports
4. Clothing and Machinery:  Imports and Exports

G. Services

1. Medicine Imports and Exports and Financial Insurances:   Imports and Exports
2. Royalties and Licence Fees and other Services:   Imports and Exports

H. Resources

1. Rainfall, Water Resources, Ground Water and Water use.
2. Forestry Industry, History, Losses and Gains

I. Fuel

1. Oil Power and Fuel use and increase
2. Nuclear Power and Electricity

J. Production

1. Meat, Vegetables and Cereals Production and Consumption

K. Work (Employment)

1. Women and men employed in Agriculture and Industrial sectors 
2. Women and men employed in Services and as Managers
3. Women and men working from home and working in Marketing
4. Unemployment
5. Teenage Mothers, Child Labour

L. Income

1. Income by men and Women
2. Income levels for the Poor
3. Living Standards

M. Wealth

1. Historical Wealth
2. 2002 Wealth Levels and growth
3. R&D Expenditure, R&D Employments , Patents and Royalties

N. Poverty

1. Human Development, Development Increase and Decrease
2. Human Poverty, Undernourishment , Underweight Children
3. Wretched Dollar ($1), Absolute Poverty (2$), Wealth Decline and Gender Empowerment.

O. Housing

1. Poor and Basic Sanitation, Sewage and Poor Water
2. City Living and Slums
3. Housing, Durable Housing, Overcrowding, General Housing

P. Education

1. Literacy Youth & Adult, Illiterate Young Women , Illiterate Women
2. Primary Education, Secondary Education, Girls not at Primary School, Girls not a Secondary School
3. Science Research, Science Growth, Tertiary Education, Women not in Tertiary Education
4. Education Expenditure

Q. Health

1. Public Health Spending, Private Health Spending, Hospital Beds, Health Service Quality
2. Midwives Working, Nurses Working, Pharmacist Working, Dentists
3. Condoms Used by Men, Condoms Used  by Women [46]

R. Disease

1. HIV Prevalence, Tuberculosis Cases, Influenza Outbreaks
2. Cholera Cases, Cholera Deaths, Childhood Diarrhea, Yellow Fever
3. Diabetes Prevalence, Unhealthy life
4. Alcohol Consumption, Men Smoking, Women smoking, Road Deaths  

S. Disaster

1. Killed by Disasters, Killed by Earthquakes, Killed by Volcanoes, Killed by Avalanches and Landslides
2. Killed by Drought, Killed by Floods, Killed by Storms, Killed by Extreme Temperatures

T. Death

1. Life Expectancy, Increased Life Expectancy, Female Life Expectancy, Maternal Mortality 
2. Stillbirths, Early Neonatal Mortality, Infant Mortality, Infant Mortality Change 
3. Mortality 1-4 Years Old, Mortality Change 1-4 Years, Old, Female Mortality 15-60 Year old, Male Mortality 15-60 Year Old

U. Biodiversity Loss

1. Species Extinct, Species In Zoos Only, Species at Risk, Species at Lower Risk
2. Mammals at Risk, Birds at Risk, Reptiles at Risk, Amphibians at Risk
3. Fish at Risk, Amphibians at Risk, Molluscs at Risk, 

V. Violence

1. Military spending 2002, Military Spending 1990, Nuclear Weapons
2. 
   

W. Pollution

1. Carbon Emissions
2. Nuclear Waste, Hazardous Waste, Waste Collected, Waste Recycled 

X. Depletion

1. National Income, National Savings, Capital Consumption, Ecological Footprint
2. Water Use

Y. Communication

1. Telephone Lines 1990, Telephone Lines 2002, Cellular Subscribers 1990, Cellular Subscribers 2002 
2. Internet Users 1990, Internet Users 2002, Personal Computers
3. Printed Media

Z. Trade

1. Primary and Secondary Exports
2. Hi-Tech Exports 1990, Hi-Tech Exports 2002

AA. Action

1. Trade Unions, Strikes and Lockouts, Right to Vote, Voter Turnout

AB. Cause of Death

1. Sexually transmitted
2. Widespread disease: e.g Malaria, Sleeping Sickness,  Schistosomias
3. Cardiovascular Diseases
4. Accidental Deaths [excluding Road Traffic]

AC. Age of Death

1. Demography (male/female)

First Report to complete

 Data

http://planet.uwc.ac.za/nisl/ESS_2012/ESS132/

Four research Questions Select/adapt = Testable (YES?NO?)

Simple

NO complex arguments

Examine data in spreadsheet

Prepare Averages, Graphs, Tables

Answer each question and interpret the results. (800 word)

Report Due next Thursday 23rd August

Reports to be printed and sent to Email knight.rich@ymail.com

15% deduction for each day the assignment is late.

DATA: Body Mass Index and Waist to Hip Ratio

Here is the link to the data.

Please check you data for completeness make changes - you have editing power.

By next week you need to post a comment on how you would analyse this data.

Look at Chapters 1 and 2 of this blog for help.

https://docs.google.com/spreadsheet/ccc?key=0Ao5VdKkjuh89dFlFNXJNUjh6OWJIQVJQMkpVWTh4Z3c

PowerPoints can be downloaded from:

http://planet.uwc.ac.za/nisl/ESS_2012/ESS132/

PowerPoint 2: Measures of location

When conducting an experiment, we collect data, usually in the form of quantitative variables that are measured or observed. These values we refer to as response variables.

Data will be either discrete or continuous. Discrete variables are obtained by counting. There are a finite, or countable number of choices available with discrete data. You can’t have 2.36 people present in the room for example.

Continuous variables are usually obtained by measuring. Length, mass, time – these are all examples of continuous variables. Since continuous variables are real numbers, we usually round them in other words, we put a boundary on the number of decimal places.

Learn more by opening up the following Google Slide Show
https://docs.google.com/open?id=0B45VdKkjuh89aGYxV2tIeW1kNVU

PowerPoint 1: Data types and representation

When conducting an experiment, we collect data, usually in the form of quantitative variables that are measured or observed. These values we refer to as response variables.

Data will be either discrete or continuous. Discrete variables are obtained by counting. There are a finite, or countable number of choices available with discrete data. You can’t have 2.36 people present in the room for example.

Continuous variables are usually obtained by measuring. Length, mass, time – these are all examples of continuous variables. Since continuous variables are real numbers, we usually round them in other words, we put a boundary on the number of decimal places.

Learn more by opening up the following Google Slide Show

https://docs.google.com/open?id=0B45VdKkjuh89c1IzbDdqX0NzVEk

Capetonians SA's fattest by Janice Keogh (Beeld 2010-09-08)

Johannesburg - About two-thirds of women in South Africa are overweight. This is just one of the findings in a survey done in July among 500 adults in the country's four biggest cities, on behalf of the pharmaceutical company GlaxoSmithKline (GSK). Altogether 72% of Cape Town's residents are overweight. Pretoria comes second at 68%, followed by Johannesburg at 59% and Durban at 52%. The study also showed that, with 61% of adults in these four cities being overweight, South Africa is the country with the third-most overweight citizens, with America coming first and Britain second. Australia is in fourth place. 

Dr Jeff King, a Johannesburg cardiologist who was involved in the study, attributes this tendency to South Africans becoming more westernised and increasingly likely to eat junk food. According to Andrea Ellens, who was also involved in the survey, the world reached a turning point a few years ago.

There are currently more overweight than malnourished people worldwide.Jonathan Girling, deputy president of consumer healthcare at GSK, said the purpose of the survey was "to determine how healthy South Africans are". According to Celynn Erasmus, a dietician, many people sacrifice their health for money. "Being overweight, obese or morbidly obese combined with stress in the workplace is a recipe for disaster."

Besides work stress, things like cold temperatures, boredom and depression also tend to make people eat more, according to the study.
Ellens says the cost of healthy food has a significant impact on people's weight. About 40% of respondents' food shopping is driven by price, and 59% feel the government should do more to prevent citizens from becoming obese, for instance by making healthier food more affordable.

Ellens also said that overweight parents have an impact on how their children eat. Children were not included in the sample, but previous studies by the World Health Organisation showed that 17% of children in South Africa are overweight.

Please complete the following information collected in class today – use the link below to determine ratios and risks

Measurements to be done in class

	Scale A	Scale B	Scale C Digital (+ 0.2kg)
Weight (Mass) kg
Height (cm)		Body Mass Index BMI		Risk
Waist (cm)	Hip (cm)	WHR (Waist to Hip ratio)		Risk
Age		Exercise (30 min or more) Circle the most appropriate	1-3 times a month	1-2 day a week
Gender (M/F)			2-4 days a week	More than 4 days a week

 Please use the link below to calculate BMI and WHR and then condition (risk)

http://www.scars1.com/CareTools/Calculators/Advanced_BMI/

Course website: http://ESS132.blogspot.com (Part of our new Carbon Campus site http://carboncampus.blogspot.com)

Where to make your measurements Male/Female

 Body Mass Index

The body mass index (BMI), or Quetelet index, is a heuristic proxy for human body fat based on an individual's weight and height. BMI does not actually measure the percentage of body fat. It was devised between 1830 and 1850 by the Belgian polymath Adolphe Quetelet during the course of developing "social physics".Body mass index is defined as the individual's body mass divided by the square of his or her height. The formulae universally used in medicine produce a unit of measure of kg/m². BMI can also be determined using a BMI chart, which displays BMI as a function of weight (horizontal axis) and height (vertical axis) using contour lines for different values of BMI or colors for different BMI categories.

Source: http://en.wikipedia.org/wiki/Body_mass_index

Waist to Hip Ratio

The WHR has been used as an indicator or measure of the health of a person, and the risk of developing serious health conditions. Research shows that people with "apple-shaped" bodies (with more weight around the waist) face more health risks than those with "pear-shaped" bodies who carry more weight around the hips.

WHR is used as a measurement of obesity, which in turn is a possible indicator of other more serious health conditions.
WHO STEPS states that abdominal obesity is defined as a waist–hip ratio above 0.90 for males and above 0.85 for females, or a body mass index (BMI) above 30.0. The National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) states that women with waist–hip ratios of more than 0.8, and men with more than 1.0, are at increased health risk because of their fat distribution. 

WHR has been found to be a more efficient predictor of mortality in older people than waist circumference or BMI. If obesity is redefined using WHR instead of BMI, the proportion of people categorized as at risk of heart attack worldwide increases threefold. The body fat percentage is considered to be an even more accurate measure of relative weight. Of these three measurements, only the waist–hip ratio takes account of the differences in body structure. Hence, it is possible for two women to have vastly different body mass indices but the same waist–hip ratio, or to have the same body mass index but vastly different waist–hip ratios.

Even in ancient civilizations globally, female representations are most often in the 0.6-0.7 range for WHR, suggesting a preference towards lower WHR.

Other studies have found that waist circumference, not WHR, to be a good indicator of cardiovascular risk factors, body fat distribution and hypertension in type 2 diabetes.

Source: http://en.wikipedia.org/wiki/Waist%E2%80%93hip_ratio

ESS132 Overview of Course and listing of assignment details) 2012

Meetings (Lectures Practicals)

THIRD TERM

First week, (Monday 3^rd period CH1, Wednesday  2^nd period SC9, Thursday 5^th-7^th Collaborative Learning Lab and Friday 4^th period  SC9)

Rest of term 3 meet Thursday 5^th-7^th Collaborative Learning Lab

FOURTH TERM

Meet Thursday 5^th-7^th Collaborative Learning Lab

Last week of term (venues to be announce, Thursday 5^th-7^th Collaborative Learning Lab)

Availability

Dr Richard Knight (General correspondence knight.rich@gmail.com submissions knight.rich@ymail.com)

Mondays 2am until 5pm (3D GIS lab ,BCB 5^th floor New Life Science Building)

Fridays 9am until 11:30am (3D GIS lab ,BCB 5^th floor New Life Science Building)

Mr Martin Cocks (Correspondence mcocks@uwc.ac.za – only after 23 August)

Thursdays ONLY (10am until 1pm – only after 23 August)

Course Outcome

The student will have demonstrated the ability to: Analyse a case study and combine content and skills that have been learnt in other modules and apply them to a particular case study.

The content to be applied will be gained from any of the other five first year Environmental and Sustainability Studies modules as well as facts pertaining to the particular case as discussed with the lecturer concerned.

Course Assessment

Introduction to Measurement and Statistics (Cape Town Student Health Survey) 25% (Due Date: 10/08/2012)

Concept Map (Hand Prepared) Globalisation Cartograms 25% (Due Date 24/08/2012)

Preliminary Research Report 25% (Dute Date09/10/2012)(This and final report = EXAM Mark)

Preliminary Research Report 25% (Dute Date30/10/2012) NOTE WE CANNOT ACCEPT LATE REPORTS

Course Website

Material will be put up at the following URL http://ess132.blogspot.com

Detailed content will be posted at this site including full descriptions of each assignment.