Lab 4: Quantitative Data Classification

1. Data Classification in ArcMap:

In mapping cost of housing, income per-capita, or population density, it is important to map with the proper data classification to avoid skewing map results as evident in the four data classification maps featured above.

Here are two scenarios for choosing the appropriate data classification, however we should be wary of possible ethical implications in mapping:

• If I were a journalist reporting on Vancouver housing prices, I would use the Equal Interval classifying map because it represents a range of equal values. I would want to emphasize the difference between the features of the housing data I am looking into. This type of classification used for mapping Vancouver housing is slightly deceptive due to its ‘equalizing’ nature, when in fact the houses in Vancouver are fluctuating at higher prices than stable data could keep up.
•  If I were a real estate agent preparing for a presentation for prospective homebuyers in UBC area (Point Grey), I will have to use the manual break classification. This is so I can control the data being presented. Manual break data classification can ignore the surrounding areas that I do not need to present, and it can compare features to specific meaningful values. However, there are ethical implications to this type of classification because Point Grey (UBC campus) is one of the most expensive housing neighbourhoods here in Vancouver city (alongside Shaughnessy and West Vancouver), and so eliminating all other neighbourhoods will only create a comparison within houses and apartments in the neighbourhood of concentration- ignoring all other neighbourhoods, possibly with lower housing prices.

 

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