Mapping the most and the least

Transcription

1 Mapping the most and the least

2 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)

3 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

4 Indicators you want to map Counts Ratio Proportion Rate (weekly, yearly) Indicators to monitor performance Completeness Timeliness

5 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

6 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

7 Counts Number of cholera cases during weeks and in Katanga, RDC

8 Counts Number of cholera cases during weeks and in Katanga, RDC

9 Area aggregation and density symbol

10 Choropleth maps Choropleth maps should not be used for mapping COUNT data

11 For counts is better to use the proportional symbol

12 .or Charts

13 Choropleth maps Are more suitable for : Ratios Proportions Rates Density

14 Number of cholera cases during weeks and in Katanga, RDC Rate x 1000

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

16 Distribution of Death by Falls by Province, Canada, 1998 Crude deaths rate per 100,000 Age Standardized Rate per 100,000

17 Descriptive Analysis of Place Use of Standardised Rates Age structure Disease occurrence varies across ages independently of place Population structure varies across places independently of disease Disease Age, independently related to disease and to location Place Confounding

18 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

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

20 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

21 Indirect standardisation

22 Distribution of Death by Falls by Province, Canada, 1998 Crude deaths rate per 100,000 Age Standardized Rate per 100,000

23 Limits of choropleth maps The values represented in on area are not uniformly distributed as represented in the map.

24 Using intervals A tricky situation

25 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

26 Quantiles Each class has an equal Number of features Mapping data regularly distributed Standard Deviation Displaying data around the mean

27 Always explore your data before to map them

28 Dot maps As a thematic map where each dot represent a value Useful in identifying location

29 Number of cholera cases during weeks and in Katanga, RDC

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31 Dot maps Beaware! In this case points are located randomly More points more cases The point does not represent the exact location Careful how do you interpret

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33 Random distribution of points

34 Random distribution of points

35 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

36 Same population

37 Different density

38 Using dots and color for place and time

39 Dots for exact location

40 Coordinates X, Y

41 Coordinates X, Y

42 Dots maps representation Very few EWAR system accurately record the exact address of residence of cases However sometime these information can be very useful in understanding the dynamic of an outbreak especially in the identification of CLUSTERS

43 Amoy Garden

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46 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

47 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

48 Choosing and using colors

49 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

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

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

52 Some colors have to alert you! RED = FIRE

53 Avoid to create confusion to the audience with many colours

54 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