Greenspace Analysis and Ranking Criteria for Potential Greenspace Polygons

 

 

Overview

 

Following the identification and mapping of the City of Atlanta’s potential greenspace, the GIS Center analyzed, ranked, and prioritized potential greenspace polygons according to their effect on three different parameters:

 

1)      Water Quality

2)      Forestry Resources

3)      Greenspace System Connectivity

 

The effect on each of the above parameters corresponds to the three objectives set out in the beginning of the project:

 

1) The protection of water quality

2.) The preservation of forest resources

3.) The promotion of a contiguous greenspace network

 

To determine a potential greenspace polygon’s “effect “ on a specific parameter, several assumptions were made:

 

Water Quality – The proximity of a potential greenspace polygon to water bodies, wetlands, and floodplains is directly related to its effect on water quality.

 

Forestry Resources – The area and type of forest cover found in each potential greenspace polygon is a measure of its importance as a forestry resource.

 

Connectivity – The area of a potential greenspace polygon and its proximity to parks, schools, and cemeteries indicate its degree of potential to become a beneficial part of a connected greenspace network.

 

Two greenspace analyses were performed for each parameter, resulting in two acquisition priority scores per parameter for each potential greenspace polygon. For example, two water quality analyses, Water 1 and Water 2, were conducted, with each potential greenspace polygon receiving two acquisition priority scores, one for the Water 1 analysis, the other for the Water 2 analysis. The scores from the Water 1 analysis and Water 2 analysis were added together to form a total water quality score. The forestry and connectivity parameters were tested in a similar manner. Total scores from the water quality analyses, forestry analyses, and connectivity analyses were added together to form a final ranking and prioritization score for each potential greenspace polygon. Potential greenspace polygons with the lowest final ranking and prioritization scores received the highest acquisition priority. Figure 1 depicts the scoring for potential greenspace polygons.

 

           

 

 

 

 

 

 

 

 

 

 

 

 

Ranking Criteria

 

A. Water Quality Analysis

 

Potential greenspace polygons were given scores and ranked based on their effect on water quality. It was assumed that the proximity of a potential greenspace polygon to water bodies, wetlands, and floodplains is directly related to its effect on water quality.  To accurately measure this effect, two individual GIS based water quality analyses[1] were performed. The first water quality analysis measured a potential greenspace polygon’s proximity to water bodies. The second water quality analysis measured a potential greenspace polygon’s proximity to wetlands and floodplains. Each potential greenspace polygon received a numeric score based on proximity to water bodies, wetlands, and floodplains. Scores for each analysis range from 1 to 4 with 1 having the highest acquisition priority.  Results of the two individual water quality analyses were combined to form a total water quality score, ranging from 2-8, with 2 having the highest acquisition priority. Ranking and scoring criteria for both water analyses follows:

 

Water 1 – Proximity to Water Bodies

 

  1. Exceptional = Abuts a US Infrastructure designated priority stream segment[2]
  2. High = Abuts another stream  / water body or is located within 500 ft. of a US Infrastructure designated priority stream segment
  3. Moderate = Located within 500 ft. of a stream / water body or located within 1000 ft. of a US Infrastructure designated priority stream segment
  4. Low = All others

 

Water 2 – Proximity to Wetlands and Floodplains

 

  1. Exceptional = Completely contains a wetland or is completely within the 100 yr. floodplain
  2. High = Abuts a wetland or the 100 yr. floodplain
  3. Moderate = Within 500 ft. of a wetland or the 100 yr. Floodplain
  4. Low = All others

 

Water Total  = Water 1 score + Water 2 score

 

      1-2 = Exceptional

      3-4 = High

      5-6 = Moderate

      7-8 = Low

 

B. Forestry Analysis

 

Potential greenspace polygons were given scores and ranked based on their type and area of forest cover. It was assumed that the area and type of forest cover found in each potential greenspace polygon is a measure of its importance as a forestry resource. Two individual forestry analyses were performed. The first forestry analysis measured the area of forest cover for a potential greenspace polygon. The second analysis measured the type of forest cover for a potential greenspace polygon. Each potential greenspace polygon received a numeric score based on the type and area of forest cover. Scores for each analysis range from 1 to 4 with 1 having the highest acquisition priority. Results of the two individual forestry analyses were combined to form a total forestry analysis score, ranging from 2-8, with 2 having the highest acquisition priority. Ranking criteria for both forestry analyses follows:

 

Forest 1 – (Forest cover / canopy area)

 

  1. Exceptional = Greater than 75 % of total area is covered by forest.
  2. High = 50 % - 75 % of total area is covered by forest.
  3. Moderate = 25 % - 50 % of total area is covered by forest
  4. Low = All others

 

Forest 2 – (Forest Cover Type)

 

  1. Exceptional = Greater than 75 % of total area is covered by either pure pine or hardwood stands.
  2. High = 50 % - 75 % of total area is covered by either pure pine or hardwood stands.
  3. Moderate = 25 % - 50 % of total area is covered by either pure pine or hardwood stands
  4. Low = All others

 

Forestry Total  = Cumulative scores from Forest 1 and Forest 2

 

     1-2 = Exceptional

     3-4 = High

     5-6 = Moderate

     7-8 = Low

 

To determine the forestry cover type and area for the potential greenspace polygons, several GIS methods were employed. First, using Erdas’ Imagine software, the mosaiked Atlanta image was clipped to the potential greenspace boundary. Clipping helped reduce work for the next step, image classification. A supervised classification[3] was then performed on the mosaiked DOQQ of Atlanta. For this project, the image was classified into eight separate classes[4], two being the most relevant, evergreen and deciduous.

 

Following image classification, the classified image was overlaid with the potential greenspace layer. A summary function[5] was performed which broke down image classes by their associated polygons in the potential greenspace layer. This process allowed for the calculation of various classification statistics for each potential greenspace polygon. Results of the forestry analysis were used to rank potential greenspace polygons based on the above forestry criteria.  

 

C. Connectivity Analysis

 

Potential greenspace polygons were given scores and ranked based on their size and proximity to parks, schools, and cemeteries. It is assumed that the area of a potential greenspace polygon and its proximity to parks, schools, and cemeteries indicates its potential to become a beneficial part of a connected greenspace network. Two individual connectivity analyses[6] were performed. The first connectivity analysis measured the size of a potential greenspace polygon. The second connectivity analysis measured a potential greenspace polygon’s proximity to parks, schools, and cemeteries. Each potential greenspace polygon received a score based on its size, and proximity to schools, cemeteries, and parks. Scores for both analyses range from 1 to 4 with 1 having the highest acquisition priority. Results of the two individual connectivity analyses were combined to form a total connectivity analysis score, ranging from 2-8, with 2 having the highest acquisition priority. Ranking criteria for both connectivity analyses follows:

 

Connectivity 1 (Proximity to existing parks, schools, and cemeteries)

 

  1. Exceptional = Abuts a park, cemetery, or school.
  2. High = Within 500 ft. of a park, cemetery, or school.
  3. Moderate = Within 1000 ft. of a park, cemetery, or school.
  4. Low = All others

 

Connectivity 2 (Size of potential greenspace polygon)

 

  1. Exceptional = Greater than 100 acres in size
  2. High = Between 50 and 100 acres in size
  3. Moderate = Between 10 and 50 acres in size
  4. Low = All others

 

Connectivity Total  = Cumulative scores from Connectivity 1 and Connectivity2

    1-2 = Exceptional

    3-4 = High

    5-6 = Moderate

    7-8 = Low

 

D. Final Greenspace Analysis

 

The final step of the greenspace analysis was the combination of results from each greenspace analysis parameter to form a final ranking and prioritization score. All scores were weighted equally. The final ranking and prioritization score was used to prioritize the potential greenspace polygons. Potential greenspace polygons with the lowest ranking and prioritization scores received the highest acquisition priority. After prioritizing the polygons, a new GIS layer was created by intersecting the potential greenspace layer with parcel data to determine which parcels to target for acquisition. The result is a list of parcels and their associated tax and ownership information that have been ranked and prioritized by their effect on water quality, forestry resources and greenspace system connectivity.

 

Final Ranking and Prioritization Score = Water total + Connectivity total + Forestry total

 

9-12 = Exceptional

13-15 = High

16-18 = Moderate

19-23 = Low

 


[1] The GIS based methods used to score the potential greenspace polygons in the water quality analyses were simple proximity, line in polygon, and polygon in polygon analysis, all performed using ArcView 3.2a.

[2]As part of the aforementioned Consent Decree, US Infrastructure (USI), an Alabama based engineering firm, was contracted to design, manage, and implement the City of Atlanta’s Greenway Acquisition Project. As part of the project, USI evaluated and analyzed water in Atlanta’s streams. A result of this analysis was a USI generated map layer that prioritized stream segments as important to protect for water quality reasons. The analysis also identified land adjacent to these steam segments that should be acquired and protected in perpetuity.

[3] Multispectral Image classification is the process of grouping pixels in an image into classes (e.g., agriculture, forest, water, etc) based on their values and location in the Electro Magnetic Spectrum. In a supervised classification, the identity and location of some of the land cover types, such as wetlands, roads, forest, are known in advance. The image interpreter evaluates these known land cover types and then assigns pixels to the class in which it has the highest likelihood of being a member.

[4] The supervised classification of the Atlanta image resulted in the formation of eight classes: evergreen, hardwoods, shrub, grasses, water, exposed earth, and shadow. 

[5] A Summary function produces cross-tabulation statistics that compare class value areas between two thematic files (in this case, the classified Atlanta image and the potential greenspace layer) including number of points in common, number of acres (or hectares or square miles) in common, and percentages.

[6] The GIS based methods used to score the potential greenspace polygons in the connectivity analysis were simple proximity, point in polygon, and polygon in polygon analysis, all performed using ArcView 3.2a. Potential greenspace polygons were also queried based on polygon size as part of the second connectivity analysis.