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Overview

Text should include: Overview, purpose, when it happens in the HPC or IM cycle

Analysis Spectrum and the HPC 

(provide details on the process for each phase/product of the HPC)

    • Exploratory
      • Aggregation,  disaggregation
      • Chronologies, timelines
      • SDR
    • Describe
      • Prioritization (sudden onset)
      • Severity (sudden onset)
    • Explain
      • Cause and effect
    • Interpret
      • Ratings, rankings and uncertainty
    • Anticipate
      • Prediction, forecasting
      • scenario building

Exploratory

Focus: identify if data required is available (credible, reliable, timely) and structure it in a way that best suit the requirement and identifies information gaps. 

Main activities and questions

  1. Familiarise yourself with the data and check its characteristics - How relevant, sufficient and reliable is the data?
  2. Clean & enrich your data to ensure it is as good as it gets - How clean and ready for analysis is the data? Do I have enough data?
  3. Are potential signals hidden in the data?
  4. Sort, aggregate and disaggregate and define suitable taxonomy of categories. Code & refine your data – Can the data be better prepared for queries?
  5. What are the main results so far?

Examples of analysis findings

  • There is a variety of information sources on food insecurity in country X, primarily from IPC, Fewsnet, WFP and FAO.
  • Some are purely observational, some are quantitative.
  • Recent figures on food security are available after a comprehensive national survey by WFP
  • Findings are mostly aligned.
  • No recent information is available from the southern region where accessibility is limited
  • There seem to be higher levels of food insecurity in rural areas.

Descriptive

Focus: What is written in these sources? What does the data tell us about a given situation? Who is affected, where, how many people? Summarise and describe the data, to reduce the amount of data and make it easier to compare. Comparison is key to analysis

Main activities

  1. Group similar observations and reduce your data - What meaningful comparisons reveal differences?
  2. Select the metric that best describes the situation – How can I summarise my data in a way that best describes it?
  3. Compare and contrast between and within groups of data to identify meaningful and significant differences and similarities - What consistent patterns, trends, or anomalies emerge from the data?

Compare to what?

  • Humanitarian standards (e.g. humanitarian conditions vs SPHERE standards)
  • Time (e.g. Pre- vs in-crisis)
  • Geographic (e.g. Governorate A. vs Governorate B.)
  • Population group (e.g. Refugees vs IDPs

Examples of analysis findings

  • There are 15 million people in country X who are food insecure.
  • The large majority is in rural areas.
  • Conflict-affected areas such as district A, B and C have the highest proportion of food insecure people about the total population.
  • The proportion of food insecure people has more than doubled in the last five years.


Explanatory

 



Interpretive: 




Anticipatory: 




Prescriptive:


Outputs/Resources

Text should include: Essential Reading, Additional Readings, Templates. Examples, Tutorials


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