We are trying to post here the most frequently asked questions about INSENSA-GIS. If you can not find the answer to your question please have a look at the help of the software or post your question in the forum.

INSENSA-GIS has been developed for the purpose of index construction and sensitivity analysis. However, it also provides several other functions such as map display and different statistical functions. Therefore, you can use INSENSA-GIS also for many other purposes.

INSENSA-GIS stands for Index and Sensitivity Analysis GIS software.

We tried to provide functions that are often used when constructing indices and doing sensitivity analyis. However, INSENSA-GIS is still under development and can be extended by other users through plug-in development. Have a look at the plug-in download section to see if the function has already been programmed by another user. Otherwise we would highly appreciate your contribution by programming your own plug-in.

Indices are created from and combine the information of different single indicators. Different indices have been developed to for example to inform policy, such as the human development index. The advantage of an index is that you create one single number per region (or pixel) that is summarizing different indicators. Nonetheless, it is important to look at each indicator individually to fully understand what your index is telling you.

Indicators are often covering different value ranges. One indicator is for example a percentage value between 0% and 100% while another indicator gives you values between 0.1 and 5. If you would add these indicators without normalizing them, the indicator with a lower range would always have a lower influence on your final outcome. Normalizing transforms your indicator to the same range, e.g. between 0 and 100. Another option would be to categorize your indicator values in the same number of classes, e.g. using 10 equal sized categories.

Weighting is usually used to describe the importance of one indicator relative to another one. If I assign a weight of 20% to indicator 1 I am considering it as being double as important as indicator 2 with 10%. If you decide that all indicators are equally important use an equal weighting scheme. Another option is to group your indicators based on what they are supposed to indicate (e.g. group vegetation density and Normalized Difference Vegetation Index as biomass indicators) and give each indicator of one group equal weights. Indicator weighting is often difficult to justify. There are some mathematical procedures, usually either based on correlations betweeen different indicators or based on expert opionions, that you could use to assess your weighting scheme.

This question depends on the specific index you want to construct. The difference between the calculation procedures is that for the additive index, all indicators are summed up after they have been weighted (weight1 multiplied with indicator value1 plus weight 2 multiplied with indicator2), while they are multiplied with the weight being the exponent to each indicator value. The different mathematical procedures will have different outcomes and it is useful to compare both. If you are using the additive index aggregation one indicator may balance out the lower performance of another indicator. This potential of compensation is limited for the geometric aggregation process.

A sensitivity analysis gives you an idea how your index changes if you exclude single indicators or if you change your weighting scheme. As weighting, indicator selection and the construction of your index algorithm (e.g. setting of threshold values) usally implies some degree of subjective decision making, a sensitivity analysis will help you to find out which indicators are changing your index most, how much the index is changing if you are altering the weighting scheme and which areas are very volatile to these changes.