Using Data for Storytelling

In the Miami Herald’s report “What went wrong” from 1992, data and maps are used to generate and introduce a problem: Why hurricane Andrew did not ravage all neighborhoods equally?  An analysis of maps of damage and wind showed that newer houses did worse than older houses in terms of resisting to the hurricane. Data in this case imposed a question that had to be investigated further by the journalists. In the following pages, observations and interviews just helped to answer the question imposed. In another passage, data is used as means to show inaccurate views and opinions from the builders. Much damage occurred in areas where the wind was under 120 mph, contradicting builders who believed the velocity of the wind was the cause for the destruction. Data findings can be very useful to show a different side of the story that cannot be seen so easily by the general public. The important aspect is using data and journalism to potentially trigger a change in building regulations in this case. 
As for the article from the New York Times, it presents the hardest places to live in the United States. To support that statement, it uses criteria based on datasets such as education level and unemployment. The first thing I would like to point out is the definition of a hard place to live. The article uses a top-down approach to answer this question. It simply uses data for that answer, without actually using any citizen’s viewpoint, or without considering that a hard place to live for one could be not as hard for other. It also contains a lot of assumptions within the analysis of data, for instance using education level to define difficulty to live. We would need further explanation on the relation between education and living conditions. To contextualize the areas and data when comparing different cities would have been a better approach to the problem. 
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