index-core

Install via npm npm install @economist/index-core

This ES module provides a way to calculate an EIU style index (what is an index?) from a couple of data sources.

• The first data source defines the index's structure, what indicators are taken into account, the nature of those indicators (min & max value, weighting etc).
• The second provides data on the entities which are being indexed (cities, countries, schools, companies or whatever) primarily what the entities score on each of the indicators but also may include background detail e.g. a region into which a country is grouped, the address of a hospital, the size of corporation and so on.

These data sources are typically derived from CSV spreadsheets and take the form of two arrays of objects with each object representing the data from one row of the CSV -- this is the default form of Javascript object produced by D3's CSV parser. Example sheets can be found in this repos data directory.

Let's look at the data sources in more depth!

Data source: Indicators

Good question! Defines the structure and properties of the index

required

• id: The id property of an tells index-core how an indicator fits into the indexes heirachy as well as providing a unique identifier. The id takes the form of a string of single dot separated word characters (a single digit or a single letter). For example if the id is 3.2.4 the indicator belongs to group 3 and subgroup 2. Similarly 3.2.4.a belongs to group 3, sub-group 2, sub- sub-group 4. Valid indicator IDs should match the following regular expression /^([\w]\.)*\w{1}$/.
• weighting: The weighting property defines how much influence an indicator has on the score of its parent group. It is typically expressed as a proportion between 0 and 1 but can be any number. So typically a group of 3 indicators might have weightings 0.33333,0.33333,0.33333. Each indicator's value will contribute one-third towards the parent group score. It might be easier to give them weightings 1,1,1 which will also mean they all contribute an equal amount but usually a 0-1 or 0-100 scheme is preferable as this is typically what the research team uses.
• type: This is required on indicators who derive their value from sub indicators and should be set to calculated. Future versions may alow other behaviors to be specified, for example inclusion/exclusion catefories.

optional

• max: (default 100) The max property specifies the maximum numeric value that an indicator can have. Whilst optional it's a good idea to specify this for clarity and most of the time it will be neccesary for the majority of indicators.That said, on calculated indicators it has no meaning or effect.
• min: (default 0) The minimum value that an indicator can take, this is more oftent han not 0.
• invert: (default false) Most indicators contribute positively towards the score of their parent group e.g. high literacy rates are considered a good thing so a big number leads to a big score. Sometimes this is not the case for example a high unemployment rate is usually considered a bad thing so we want high number to contribute negatively to the parent group score. This is achieved by setting the invert property to true.
• diverging: (default false) This can be used when an indicator wants to measure the degree of centrality. For example gender balance of a company might be a value between -100% for all male and +100% for all female so we might consider 0 (representing parity) to be the ideal value, in a diverging indicator a value of -20% will be treated the same as +20%. Typically (e.g. in the preceding example) you also want the indicator to be __invert__ed so that a low value is good. Note: 0 is always the central value. It's possible to imagine an indicator which, for example, might range between 65mins and 240mins where the ideal value is 90 mins (i.e. the best length for a feature film) but this is not currently supported.

Data source: Entities

Defines the things which are being indexed, the content.

required

• name: a unique identifier string, this is normally the name of an countrty or city or whatever but can be anything as long as it's unique.
• indicator properties: Each entity in the index needs to have a property for each indicator which belongs to the main index structure named to match the ID (e.g. 1.2.3, 4.2.3 etc.)

optional

Everything else.

There will often be ancillary imformation associated with each entity, things like a description of the entity, some background indicators, or categorisation information. These can all live in the entities sheet.

---

API

indexCore(indicators:Array, entities:Array, [indexMax:Number=100],[allowOverwrite:Boolean=true],[clamp=false])

indexCore constructs and calculates an index it returns an index object allowing access to results of the calculation for all the entities at a give indicator level, the structure of the index as a whole and methods for adjusting indicator values and weightings.

indicators is an array of indicator objects.

{
  id:String,
  weighting:Number,
  type:["calculated"|"discrete"|"continuous"]
  max:Number,
  min:Number,
  invert:Bool,
  diverging:Bool
}

entities is an array of enetity objects

{
  name:String
  1.1.1:Number, //eg
  1.1.2:Number,
  etc.
}

Take a look at the data source descriptions for a fuller description of the properties these objects might take: indicators entities

indexMax, optional. is the maximum value for the index score (minimum is always 0) by default this is 100, other typical values are 1 and 10.

allowOverwrite, optional. By default this is set to true ensuring that all calculated values will be calculated by the module. If set to false, calculated values that are supplied in the entities sheet will take precedence. Most of the time you don't want to do this but it might be a handy escape hatch.

clamp, optional. By default this is set to false. Indicator values above the max or below the min values for that indicator will not be constrained therefor normalised values may lie outside the expected range. Set to true if you want you values constrained.

indexCore.adjustValue(entityName:String, indicatorID:String, value:Number)

A function that allows the user to adjust an entity's (entityName) indicator (indicatorID) score to a specified value. Calculated values for that entity are re-calculated using the new value. If the function is called without value the indicator is reset to it's inital value. If the function is called without indicatorId or value all indicators on the entity are reset to their initial values. As a convenience the function returns an object with all the recalculated values.

NOTE: whislt the old value is retained there's currently no way to reset it.

indexCore.adjustWeight(indicatorID:String, weight:Number)

A function that allows the user adjust the weighting of an indicator to give it a greater or lesser importance in theindex as whole. The index is recalculated after calling this function.

indexCore.filterIndicators([exclude:function])

Allows the user to specify a predicate which excludes certain indicators from the calculation e.g.

// if the indicator includes "b" in its  ID then ignore it
indexCore.filterIndicators(indicator => String(indicator.id).indexOf('b')>0 )

You can clear the exclusion by calling the same function with no arguments

// if the indicator includes "b" in its then id ignore it
indexCore.filterIndicators()

indexCore.getEntity([entityName:String]):Object

indexCore.getEntityIndicator([entityName:String], [indicatorId]):Object

This function returns the value of a given indicator (indicatorId) for a given entity (entityName) if a user set version of the indicator exists, it is that which will be returned.

indexCore.getEntities():Array

indexCore.getIndexMean([indicatorId:String], [normalise:Boolean=false]):Number

indexCore.getIndicator():Object

indexCore.getIndicatorLookup():Object

indexCore.indexedData:Object

An object where each property is an entity in the index and that entity has properties for each of the properties in the indicators spreadsheet and any calculated values.

indexCore.indexStructure:Object

An object representing the structure of the index r Returns the mean value for a given indicator accross all entities in the index which posses that value. Note that if an indicator does not posses that value it's excluded from the calculation and a sane result should be returned. Providing no indicatorId will result in the mean of the top level index scores being returned. The second argument determines whether the result will be expressed as normalised (which is the default) or in the raw quantities of the indicator. i.e. if an indicator represents the number of hours of sunlight it could be better to express it un-normalised as in the mean number of hours rather than normalised to between e.g. 1-100.

---

What is an Index?

Good question!

An Index is a set of entities (cities, countries, educational institutions etc) and associated scores. The scores are a measure of the entities success in a particular domain (effective water usage, institutional democratic accountability, academic results, etc.). They are typically derived from a mix of quantitative factors and qualitative factors.

Examples of indexes include...

• The Inclusive Internet Index
• The Water Optimisation Index
• The Democracy Index

Indexes are structured like trees. Each entity's headline score is calculated as the weighted mean of several branches scores which in turn are calculated from the weighted mean of a set of sub-branches and so on until we reach the "leaves" – indicators which the researchers quantify and score.