1 Mapping tehcniques Choropleth mapping Data classification Attribution (by) Licensees may copy, distribute, display and perform the work and make derivative works based on it only if they give the author or licensor the credits in the manner specified by these. Mrs Diana Makarenko-Piirsalu MSc in Landscape Ecology and Environmental Protection Geolytics OÜ Mere tee 15, Saviranna, Jõelähtme vald, Harjumaa, ESTONIA Mob ESTP Course: Use of GIS in NSIs – Representing statistics on thematic maps, Kongsvinger, Norway, 5th to 7th of March, 2013

What are choropleth maps? On the choropleth map the phenomena is represented based on areal units, which are classified based on some indicator (qualitative or quantitative indicator). Areal units or regions are distinguished from each other based on: –colour –pattern –using 3 D effects. Choropleth maps should support comparison of phenomena in geographic space and help representing regional differences. 2

When to use choropleth technique? Choropleth maps should be used for phenomena that have spatial variation that coincide with the boundaries of the spatial area used for map. However, this is seldom the reality. Most often choropleth maps are representing typical value for the region not spread uniformly within the region. 3

When to use choropleth technique? Choropleth map is suitable for mapping discrete phenomena. Special attention should be made then using absolute numbers. In most cases it is not proper to use absolute numbers in choropleth map. To make phenomena comparable for administrative units it should be quite often standardised. The main ways, how the data are standardised are: –dividing the indicator with the area  Number of population / area of the administrative unit –using ratio of two raw totals  Ratio of males to females for example 4

Inflation rate in Source: Statistic Switzerland

Data classification Classed vs unclassed maps. What is the purpose of the map? –Unclassed map might support data exploration –Classed map supports easy of understanding spatial pattern For statistical thematic maps data are classed using methods: –Equal intervals –Natural brakes –Quantiles –Optimal (Median) –Mean - Standard deviation –Maximum Brakes For this course we consider more closely first 3 ones - supported by SW 6

How many classes should be used? Number of classes depends on the data and the purpose of the map In general it is not useful to use too many classes as this will make map reading more difficult for human being Opposite is not also useful as this will not reveal the geographical pattern Normally, it is advised not to use more than 5 – 6 classes Using for example odd number of classes is more common as this will reveal the mean better. 7

Equal intervals Number line is divided between min and max value into equal intervals. 1-5; 6-10, 11-15, 16-20, Max value – Min value / number of classes 1316,4-3,2/5 = 262,6 8

Equal intervals Advantages: –Easy to calculate also manually –Easy for map users to interpret –Legend does not contain missing values (gaps) –Unusual data value “peaks” are distinguished in separate class Disadvantages: –Class limits fail to consider how the data are distributed along a number line –Some classes might not have an observation within them 9

Natural brakes Considers “natural” grouping of the data. Clustering of data The classes are divided in the places of data distribution where the data have the biggest difference. Minimizes the difference within the classes and maximises the difference between classes. 10

Natural breakes Advantages: –Easy for map users to interpret –Legend does not contain missing values (gaps) –Similar values are grouped –All classes contain observations (at least 1) Disadvantages: –Subjective 11

Quantiles 12 Number line is divided based on number of observation. In each class contains of equal number of observations. Total number of observations/Total number of the classes For Example: 35 / 5 = 7 7 observations will be placed into one class

Quantiles Advantages: –Easy to calculate, also manually –Supports analysing the map as percentage of each observation within the classes is the same. For example we can use in case of 5 classes upper and lower 20 % of data. –Median is logically associated with the class. In vase of odd number of classes median is the middle class. In case of even number of classes median will fall within the two middle classes. –Each class will contain an observation Disadvantages: –Fail to consider how the data are distributed along the number line for example ( two peaks of population density data are included within the same class with remarkable lower values. –Legend may contain gaps 13

Comparison of methods 14 Source: T. A. Solcum, Thematic Cartography and Geovisulatization, 2009

THANK YOU! 15 Mrs Diana Makarenko-Piirsalu MSc in Landscape Ecology and Environmental Protection Geolytics OÜ Mere tee 15, Saviranna, Jõelähtme vald, Harjumaa, ESTONIA Mob ESTP Course: Use of GIS in NSIs – Representing statistics on thematic maps, Kongsvinger, Norway, 5th to 7th of March, 2013