4.4 Research methodology
4.4.7 Secondary data analysis
Analysis of secondary data was conducted using change analysis of aerial photographs and proximate premium analysis which utilised the Tax Valuation Roll data. The aim was to understand the changes that have occurred in the GGEP open space ecosystems since the GGEP project started implementing management activities in 2010. In addition, proximate premium analysis was conducted to understand the economic impact of the GGEP on proximate properties.
4.4.7.1 Change analysis methodology
According to Garzon-Lopez et al. (2013: 308), remote sensing techniques “....offer potential alternatives for mapping species distributions over large areas.” One of such remote sensing techniques is aerial photo interpretation. Aerial photographs are an important resource that has long been used and are still used in environmental management to detect change in land use and land cover (Roxburgh, 2008; Morgan et al., 2010; Bartholomeus and Kooistra, 2012). The use of aerial photographs in remote sensing is becoming valuable in environmental change studies due to their high level of spatial and radiometric resolution (Gienko et al., 2008; Morgan et al., 2010). Thus, the high resolution images provided by aerial photographs enable researchers to identify objects; land use/land cover either at a small or large-scale and availability of images for long periods of time (Roxburgh, 2008; Morgan et al., 2010). As such, aerial photographs are a valuable source of data for conducting change analysis or time series analysis (Gienko et al., 2008; Roxburgh, 2008). In order to conduct change analysis, two main methods are employed (Gienko et al., 2008: 53),
i. Inherent use of the primary image data (mostly for visual detection of changes) such as pseudo- colour composites, dynamic toggling, basic computer-assisted methods such as image subtraction and differencing.
ii. Use of derived products for advanced quantitative change analysis GIS-based vector analysis of digitised time series imagery and comparison of results of automated image classification either in raster or vector form.
However, the most common method for detecting environmental change quantitatively is the use of “GIS- based vector analysis of digitised time series of imagery” (Gienko et al., 2008: 54-55). This study adopted
the GIS-based vector analysis of digitised time series of imagery to map out and quantify the changes that occurred in the GGEP since the implementation of the GGEP project in 2010 to 2012. Thus, two aerial photographs for the year 2010 and 2012 were used. The rationale for using these two reference years is that 2010 is the start year for implementation of restorative activities while 2012 was supposed to be the last year for the pilot phase of the GGEP project. Since the end of the pilot phase should have culminated in a referendum by all property owners to decide whether the project would continue beyond the pilot phase or not, it was expected by eThekwini Municipality and the management team that activities conducted between 2010 and 2012 would be sufficient to convince property owners to vote in favour of continuing with the project.
Thus, two geo-referenced digital colour aerial photographs were obtained from eThekwini Municipality’s EPCPD for the years 2010 and 2012. The images were projected using the Universal Transverse Mercator and taken at a scale of 1:469 503. The following steps were followed:
i. Since digital aerial photographs were used, the various land use/land cover were mapped out within the GGEP for each photograph. Various land use/land cover polygons representing the different classes of land use/land cover were mapped out. The land use/land cover classification used was adopted from the eThekwini Municipality classification system used for the GGEP as shown in Table 4.6. This classification system was used for easy comparisons in data analysis of data computed using GIS change analysis and secondary data compiled by the GGEP management.
Table 4.6: Land use/land cover Classification for the GGEP (Adapted: GGEP Management Activities Maps, 2010)
No. Land-Uses/Land-Cover Description according to mapping criteria 1 Suburb Built up residential area including utilities and
facilities such as schools
2 Forest Trees with touching canopies
3 Grassland Areas without trees
4 Woodland Trees with canopies that do not touch mostly composed of gum (Eucalyptus species)
5 Wetland Water bodies including marshlands/swampy areas
ii. The resultant land use/land cover maps (from 2010 and 2012 photographs) were used to calculate the area of each polygon.
iii. The land use/land cover classes were aggregated to get the total area for each class and two tables were generated for each aerial photograph.
iv. The total area of each class was compared for each aerial photograph to assess the change.
GIS maps were generated for the years 2010 and 2012 in order to obtain visual impressions of the ecosystem units mapped in the aerial photograph analysis. The maps depicted the changes computed in hectares for each ecosystem thus visually conveying the changes in the GGEP open space observed for the period 2010 to 2012.
4.4.7.2 Property value analysis: proximate premium analysis
This study employed the proximate principle proposed by Crompton (2004: 9) as described in Tables 4.7 and 4.8. As stated previously, the Tax Valuation Roll provided the main data required for analysis while Crompton’s (2004) proximate principle method provided the open space quality scale to use in the analysis.
Table 4.7: Open space quality scale for determining proximate premiums (Adapted: Crompton, 2004: 9)
Open space Quality
Description of open space Rating
(%) Unusual
Excellence
A signature open space; exceptionally attractive; natural resource based;
distinctive landscaping and/or topography; often mentioned in sales advertisements for nearby properties; well maintained; genuine ambiance;
engenders a high level of community pride and “passionate attachment.”
15
Above Average
Natural resource based; has charm and dignity; regarded with affection by the local community; pleasant, well maintained.
10 Average Rather nondescript; not really “noticed” by the local community; adequately
maintained; no distinguishing features.
5
The percentages given in Table 4.7 rates the type of open space based on its quality as per description of open space given. Thus, rating the open space quality is the first step towards computing the value of properties attributable to the open space. After determining the quality of the open space, the researcher followed the procedure for computing the property value attributable to the GGEP open space provided in Table 4.8.
Table 4.8: Steps in calculating an estimate of the impact of open spaces on the property tax base (Adapted from Crompton, 2004: 9)
Number Steps
1 Identify and grade the open space quality on the five point scale shown in Table 4.3.
2 Draw a 3 block or 152 metres travel radius around the open space, which was classified in the three highest quality categories.
3 Aggregate the assessed value of all single-family homes within each of the three block (152 metres) radii, using data from the local tax assessor’s office.
4 Apply the percentage premiums suggested above (15%, 10% or 5%) to the aggregate value calculated in step 3.
5 Aggregate the premium calculated in step 4. This figure represents an estimate of the overall change in property value attributable to the open space examined.
6 Multiply the aggregated premiums calculated in stage 5 by local property tax rates imposed by all taxing entities to estimate the total positive impact of parks on the property tax base.
7 Compare the aggregated premium calculated in stage 6 to:
The annual debt charges incurred in the acquisition and development of those parks and open spaces; and
The annual cost of maintaining those parks and open spaces.
The proximate principle methodology for determining the value of properties attributable to an open space as proposed by Crompton (2004) provides an easy way when compared to the hedonic methods.
This is because the hedonic methods require “considerable skill in computer mapping and the use of statistical techniques, and it is time consuming” (Crompton, 2004: 19). Despite that the proximate principle only provides a rough estimate of the actual impact, it is a good method to use in communities requiring an understanding of the impact of an open space on property value (Crompton, 2004).