Table 1.
Variable | Rating Scale |
Criteria | Scale | Method/Rationale | References |
---|---|---|---|---|---|
| |||||
Population | 1 | 0–250 persons | Census block | The H-G MSA census block data was compiled and classified into 5 intervals. Lowest population areas were ranked as lower development potential while higher population areas were given the highest ranking score. This was done because, currently, the density of the region is quite low compared to many large cities striving for sustainable growth. Coupled with the large amount of vacant land in the region (40%), the research assumed that the high population areas still had ample amount of room for future growth. Also, because we used data at the census block scale, the population densities will fluctuate in different areas within these blocks. To assign population per cell, cells were assigned with population values at the census block scale. Each block was then given an interval population range and each interval was assigned a score. | Dueker and Delacy 1990 |
2 | 250–500 persons | ||||
3 | 500–1000 persons | Allen and Lu 2003 | |||
4 | 1000–2000 persons | ||||
5 | 2000–5,930 persons | Newman et al. 2016c | |||
| |||||
Soil | 0 | Not rated | Municipality | Each soil type was researched individually based on the code provided and ranked according to its pre-identified development potential. Soils were only given a 3-point maximum score based on the large scale they were analyzed. Because the data was not as fine grained as necessary, its influence was limited purposely by the analytical approach. | Karlen et al. 1997 |
1 | Very limited | ||||
2 | Somewhat limited | Wu 1998 | |||
3 | Not limited | ||||
| |||||
Property Value | 1 | 0–8,500 (dollars) | Parcel | This variable was extracted from the parcel data and classified according to Jenks natural breaks. Higher property value areas were assumed to have a higher development potential, while in lower valued areas it was assumed the inverse. | Dueker and Delacy 1990 |
2 | 8,500–40,000 (dollars) | ||||
3 | 40,000–150,000 (dollars) | Newman et al. 2016c | |||
4 | 150,000–650,000 (dollars) | ||||
5 | 650,000–3,000,000 (dollars) | ||||
| |||||
Land Cover | 0 | Salt marsh/open water | 30 × 30 meter pixels | While all other datasets were initially assessed as vector data, then converted to raster data for suitability overlay, land cover was analyzed consistently as raster data. In this case, currently developed areas were deemed highly developable while periodically flooded and aquatic areas were deemed to have little to no development potential. Sparsely vegetated areas were also given a high ranking score while forested and more densely vegetated areas, a lower rank. | Van der Merwe 1997 |
1 | Temperate flooded and swamp forest | ||||
2 | Warm and cool temperate forest | Want and Moskovits 2001 | |||
3 | Temperate grass land/meadow/shrub land/boreal shrub/herb coastal vegetation/warm semi desert shrub and grass land/ herbaceous agriculture/ introduced semi-natural | Allen and Lu 2003 | |||
4 | Developed and Urban/recently | ||||
5 | Modified | ||||
| |||||
Land Use | 0 | Water/undevelopable | Parcel | Rankings for each individual land use were based on the assumption that areas currently within land uses designated for future development purposes would have the highest development potential. Vacant land uses deemed as developable were also given high scores. Undevelopable parcels or those within a water body, land uses designated as open spaces and those devoted to institutional uses were deemed as less developable. | Van der Merwe 1997 |
1 | Parks/open spaces | ||||
2 | Government/medical/ educational | Wu 1998 | |||
4 | Vacant developable | ||||
5 | Residential/industrial/ commercial | ||||
| |||||
FEMA Flood Plains | 1 | A/AE/AO/VE/D | Region | Lands outside of the 100 and 500 year floodplains were designated as higher developable lands while areas within the 100 and 500 year flood plains were ranked as less developable. Again, due to the large scale of the flood plain data, scores were only provided with a maximum of 3 for this variable. We also believe that land cover, land use, and conservation areas capture some of the information provided by this data set. | Allen and Lu 2003 |
2 | X500 | ||||
3 | X100 | Brody et al. 2012 | |||
| |||||
Hurricane Risk Zones | 1 | Risk 5 | Region | Each hurricane risk zone is based on the category of hurricane (1, 2, 3, 4, or 5) which would impact a certain set of land. In this case, the development potential rankings were scored assuming that the lower the risk of hurricane impact, the higher the development potential. | Allen and Lu 2003 |
2 | Risk 4 | ||||
3 | Risk 3 | Brody et al. 2012 | |||
4 | Risk 2 | ||||
5 | Risk 1 | ||||
| |||||
Conservation Areas | 0 | Wetlands/refuges/state parks/ protected areas | Because the intent for this research was to ultimately create a landscape corridor model linking patches, conservation areas were listed as having no development potential. Lands outside of these parcels were given a score of two at maximum because the other variables utilized capture their potential more in-depth. In this case, we sought to only represent the need to protect conserved green spaces. | Van der Merwe 1997 | |
2 | Remaining areas | Allen and Lu 2003 | |||
| |||||
Proximity to Amenities | 1 | Remaining areas | Because existing amenities can serve as anchors spurring new development, we created buffer zones around current landmarks and civic institutions. Areas within these buffer zones were scored at a higher level than those outside of them. | Wang and Moskovits 2001 | |
4 | Library/museum/parks/ hospitals | ||||
Birch et al. 2013 | |||||
Newman et al. 2016c |