Table of Figures and Tables
Step 5: Data Analysis and Mapping
With all uncertainties captured or accounted for, the final step is to analyze the data and display the data in an organized manner.
Mapping
GIS systems provide the ability to map the findings and find primary locations future development.
Through ArcGIS, converting the Excel file to .CSV allows for the data to be input as a data table for the map. To ensure mapping is possible, the original GIS tag for each parcel must be retained in the
45Partially restricted parcels (wetlands) are parcels that were initially estimated to produce a large number of new parcels.
However, wetlands considerations reduce the amount of available land that is buildable to where the original estimate far exceeds the potential of the parcel. When calculating maximum buildout, the 2018 team assumed that 25% of all potential new parcels (from current code 11 parcels) could actually be developed.
16 data set46. This enables the software to pair the base map tax parcels to the dataset. Sorting and highlighting along desired characteristics47 allows for specific categories to be mapped with ease. It is also possible to take highlighted parcels and create a stable layer containing only the desired
parcels48.
Separate layers allowed for split viewing and analysis, such as areas with greatest potential for a specific types for dwellings. Similarly, overlaying a district layer would enable defined areas to be viewed.
The 2018 Nantucket Buildout Analysis created the following maps to exemplify the processes described:
Figure 4: Second Dwelling Parcels Figure 5: Combined Parcel Types
The left map shows one layer of only Second Dwelling Eligible parcels. This was defined in the data as entries as Second Dwelling Eligible = 1. The right map shows all 3 layers: Second Dwelling Eligible, Subdivision, and Undeveloped.
Methods of Comparison and Analysis
For the data output of a buildout analysis to be effective, there are two categories of comparison to be developed: internal or external.
Internal Comparison: The data is compared to itself by district, zone, total, or other comparative identity of the data. Finding appropriate ratios or percentages of the total may be useful statistics to describe future development by location. The percentages would add to 100%.
External Comparison: The data is compared to another set of data, preferably data that is imbedded in current assessor data, such as number of current dwellings. This enables the data to be shown as growth from present day. These percentages would not add up to 100%.
46Do not change the name of the GIS ID column.
47It is possible to highlight both from the map and from the table. The 2018 team utilized the table to highlight entries that fit some certain criteria, i.e. Subdividable = 1, to plot all potential subdividable parcels.
48Once the data is highlighted, right click the tax parcel data layer from the layer menu. Select “create new layer from selection” to create the layer of desired data.
17 Internal comparison is beneficial because it splits the data into smaller groups. The processing in Step 3, though comprehensive, only finds a grand total of all affected parcels in the region.
Organizing findings based on internal comparisons allow the data to be more easily communicated and explained. There are two primary ways to compare data: by district49 or by zone. Each informs a different point of view.
By District: This method informs where, geographically, new dwellings (or other development) can be seen50. When communicating this data, attributing values to a well-known and defined area is particularly useful.
By Zone: This method informs the effectiveness or potential a specific regulation category, being a zone51. In communicating this data, it can be easy to estimate how changing zoning would affect the potential for growth. Area change in zoning region would not be captured in this analysis.
It may also be beneficial to display data both ways, either in the same or in separate charts; this will depend on the breadth of information used in compiling these charts.
The 2018 Nantucket Buildout Analysis team found internal and external results by both district and zone. The chart headings for the District Chart are shown below:
Table 1: District Chart Heading
There are two distinct sections to the header: the district (or zone) and the maximum buildout. This enables additional analyses to be shown in the same table while still remaining separate.52 The leftmost column is the district or zone being analyzed. The row and column descriptions are as follows:
Grand Total: The sum or analysis of all districts analyzed.
District A: The set of values for a specific district.
A = Sum of potential new dwellings across all districts (see D). This is a numerical data output.
B % = Sum of % Dwelling by district (see E). All districts will sum to 100%. This is an internal comparison.
49A district is an identifiable geographic region of the town/city that is being analyzed; it might be a village or cardinal direction.
It has defined edges that are well known. District must also be extracted from ArcGIS through individual parcel selection or shape outlines, then be input into the master file.
50It is possible to find values such as these by PivotTables in Excel, where the District is a specified row in the output table.
51Similar to the above footnote, except the Zone is a row of the output table.
52The 2018 team analyzed present day Nantucket and created growth scenarios for 2030. Maintaining everything in one chart made comparison from present to future (maximum and scenario) easier to compare and present as a comparison.
18 C % = Potential New Dwellings (A) divided by Current Dwellings (not in display). This value can be greater than 100%. This is an external comparison.
D = Potential New Dwellings in District A. Is the sum of all potential dwelling source values. This is a data output.
E % = Potential New Dwellings in District A divided by Sum of Potential New Dwellings (A). This is an internal comparison.
F % = Potential New Dwellings in District A (D) divided by Current Dwellings in District A (not shown).
This is an external comparison.
A simplified version of the 2018 analysis table is shown below.
Table 2: Simplified 2018 Maximum Buildout Table