Mapping the most and the least Why do you make a map To - - PowerPoint PPT Presentation

Mapping the most and the least

Why do you make a map

• To communicate information at a glance
• To explore the data to see what

patterns and retionships you can find

• To develop hypothesis (will be topic of next module)

Making a map

• The first real decision we have to make

in designing a map is:

– What kind of data we want to present – What type of map to use

Indicators you want to map

• Counts
• Ratio
• Proportion
• Rate (weekly, yearly)
• Indicators to monitor performance

– Completeness – Timeliness

Choropleth maps

• On this type of map each area for which

data is available is presented by a colour which represents the area's value.

• Is probably the most commonly used

it is easy to read and good at presenting patterns

Choropleth maps problems

• Firstly the patterns presented are very

much dependant on the way the ranges cut up the data

• Secondly can badly mis-represent data

if wrongly used there are some types of data for which this type of mapping just isn't suitable

Counts Number of cholera cases during weeks 47-2001 and 9-2002 in Katanga, RDC

Counts Number of cholera cases during weeks 47-2001 and 9-2002 in Katanga, RDC

Area aggregation and density symbol

Choropleth maps

• Choropleth maps should not be used

for mapping COUNT data

For counts is better to use the proportional symbol

….or Charts

Choropleth maps

• Are more suitable for :

– Ratios – Proportions – Rates – Density

Rate x 1000 Number of cholera cases during weeks 47-2001 and 9-2002 in Katanga, RDC

Population density / SqKm in Katanga 1998 Limits: the density is considered uniform in each polygon

Distribution of Death by Falls by Province, Canada, 1998

Age Standardized Rate per 100,000 Crude deaths rate per 100,000

Descriptive Analysis of Place Use of Standardised Rates

Age structure Disease Place

Population structure varies across places independently of disease Disease occurrence varies across ages independently of place

Confounding

Age, independently related to disease and to location

Descriptive Analysis of Place Use of Standardised Rates

• Standardisation

– Direct – Indirect

• Value of rate affected by the reference population
• Kind of weighted average of the disease occurrence

which allows for comparing disease risks in areas with different underlying population structure

• Count and RATES may be more useful to allocate

resources

Standardisation

Assess the risks of transmission across geographical after Controlling for age and/or sex potential confounder Simpson paradox

Direct standardization

* 100,000

The reference population can be an external population used at country level, such as the country population, or some International reference populations to allow international comparisons, OR the average population in the 2 district as in our example, if the objective is simply to compare the 2 areas

Indirect standardisation

Distribution of Death by Falls by Province, Canada, 1998

Age Standardized Rate per 100,000 Crude deaths rate per 100,000

Limits of choropleth maps

• The values represented in on area are

not uniformly distributed as represented in the map.

Using intervals

A tricky situation

Equal Area

The total area in each group is approximately the same

Equal Interval

The difference between high and low is the same Mapping continuous data

Natural interval

Breaks are set where there is a jump Maximize thet difference betwen classes Places clustered values in the same class

Quantiles

Each class has an equal Number of features Mapping data regularly distributed

Standard Deviation

Displaying data around the mean

Always explore your data before to map them

Dot maps

• As a thematic map where each dot

represent a value

• Useful in identifying location

Number of cholera cases during weeks 47-2001 and 9-2002 in Katanga, RDC

Dot maps

• Be aware !

– In this case points are located randomly – More points more cases – The point does not represent the exact location – Careful how do you interpret

Random distribution of points

Random distribution of points

Dot density map

• Divides the value of polygon by the

amount represented by a dot

• 1 dot 200 people
• A polygon 6000 people
• = 30 dots in the polygon

Same population

Different density

Using dots and color for place and time

Dots for exact location

Coordinates X , Y

Coordinates X , Y

Dots maps representation

• Very few EWAR system accurately

record the exact address of residence

• f cases
• However sometime these information

can be very useful in understanding the dynamic of an outbreak especially in the identification of CLUSTERS

Amoy Garden

Mapping place and time

• Displaying place and time

characteristics of the distribution of a disease is a very effective way to grasp the dynamic of the disease transmission

What makes a good statistical map?

• Should represent the data in a truly way
• Should be easy to understand and use
• Should give an overview of the

information

• Should be pleasing to look

Choosing and using colors

Choosing and using colors

• People see colours differently and have

different reactions to colours

• Think about how the user is going to

interpret and react to the colours

In general it is a good idea to use darker more intense values for high values.

You can also associate the color with the intrinsic message of the value represented (good, bad) Good, light colors Bad, dull colors

Some colors have to alert you ! RED = FIRE

Avoid to create confusion to the audience with many colours

Summary

• Use bright, nice colours for good things.
• Use dark and ugly colours for bad

things

• Normally use high values of the

dominant colour for the higher values

• Just try and give the right impression

when the user first looks at the map