Geographical Information Systems, Building Information Modelling and Economic Cost Benefit Analysis

by | May 14, 2014 | Uncategorized

In several posts (also here, here, here, here, … ad nauseam) I have discussed how Building Information Modelling (BIM) and Geographical information Systems (GIS) fit naturally with Cost Benefit Analysis (CBA). CBA-BIM, as I have dubbed it, has over a 15-year history in the literature. In this post I review the literature behind CBA-BIM.

In the next post I’ll discuss a critical link between BIM/GIS and CBA from this literature – the willingness to pay (WTP) distance decay function.

The two principal uses of GIS for economists are that economic value varies with distance and by affected group. These concept are embedded in this WTP function. In this post I’ll outline the literature behind how distance affected the amenity value and by equity.

"Modelling environmental equity: access to air quality in Birmingham, England" Julii S Brainard, Andrew P Jones ô, Ian J Bateman, Andrew A Lovett, Environment and Planning A 2002, volume 34, pages 695 - 716
Who is hurt by pollution?
“Modelling environmental equity: access to air quality in Birmingham, England” Julii S Brainard, Andrew P Jones &, Ian J Bateman, Andrew A Lovett, Environment and Planning A 2002, volume 34, pages 695 – 716

“Given that GIS data are spatial, a natural use is to measure the distance between observations or between observations and other features of interest. These distances could be physical distances, network distances (e.g. along a transport network) or involve some more general concept of social distance.” Overman (2006)

GIS has been used to answer seven questions. These questions have been used in spatial economic analysis:

Identification

  • What is at a particular location? GIS can be used to map conversion probabilities by linking land-cover data with a model of conversion potential (Fleming 1999 – note – all references are shown as full citations below with links to the papers). An econometric model of land value’s determinants including the value of rural conservation zoning and clustering (or unoccupied or undeveloped land close by).

Location

  • Where does a certain type of feature occur? identification of the spatial distribution of neighbourhoods containing toxic landfill sites in studies of environmental equity to pollutant exposure. The ethnic make-up of neighborhoods over time both before and after landfill sites are sited was tested for causation (Pastor et al. 2001).

Trend

  • Which features have changed over time? Modelling and prediction of urban growth patterns (Irwin 2003).

Routing

  • What is the best way to travel between two points? Studies using travel cost or hedonic pricing methodologies, where the generation of measures of the spatial accessibility of facilities is important (Brainard et al. 1999). In such cases GIS can be used to generate indicators of travel times and distances which, if required, may be converted into predictors of travel cost.

Pattern

  • Is there a spatial association between two types of features? GIS can assist with the calculation of configuration measures such as fragmentation indices (Bockstael 1996).

Buffer

  • What features fall within a selected distance from a specified feature? Delineation of riparian buffers that are designed to intercept and sequester pollutant runoff around streams, lakes, or reservoirs (Belt et al. 1992) but also has application in the generation of noise contours around road networks (Bateman et al. 2001)

What if

  • What will happen if a particular change takes place? Determination of the likely increase in visitation rate that may be associated with the upgrading of a recreational facility, to the identification of the optimal location for the siting of a new eco-industrial park in order to minimize environmental dis-amenities from commuting activities (Carr 1998).

Source: Based on Rhind (1990); Kraak and Ormeling (1996) as reproduced in Bateman et. al. (2002). Examples from Bateman et. al. (2002)

Amenity Value

In environmental economics the value of amenities (or dis-amenities) is often estimated with hedonic pricing models. Where value is a function of the characteristics of the amenity and regression analysis is used to sort out how much each characteristic is worth. Housing is the easiest example to think of. When buying a house the price (value) is a based on the number of bedrooms, bathrooms, age of the heating system etc. It is also determined by closeness to schools, parks etc. These distance calculations can be much more precise with GIS. Some examples of calculations of value using these hedonic price models and GIS distance measures:

  • Car travel times – Lake et al. 2000; Orford 1999.
  • Shape of the nearest wetland areas in addition to their proximity – Mahan et al. (2000).
  • Forest amenities described by an index variable that measures the ratio of forest acreage to the squared distance away from the home – Powe et al. (1997).
  • Measures of percent open space, diversity, and fragmentation of land uses around land parcels. The diversity index was designed to signify the extent to which the landscape was dominated by a few or many land-uses, whilst the fragmentation index was set to represent the variance in landscape parcel types present within an area. Diversity and fragmentation are valued in highly urbanized areas where they represent amenities- Geoghegan et al. (1997).
  • Viewshed (a measure of the features that are visible from a viewpoint) studies find that the type and (where assessed) the quality of view available from a property is a significant determinant of its price – Lake et al. (2000).
  • Permanent open spaces are three times more valuable to local residents than those which are potentially available for development – Geoghegan (2002).
  • Traffic and road network data generate estimates of noise pollution at each property – Lake et al. (2000) and Bateman et al. (2001)
  • Proximity to hazardous waste – Ihlanfeldt and Taylor (2001). Hazardous waste sites are found to negatively affect the market values of nearby commercial and industrial properties. Estimates of the total value losses caused by many of the sites are sufficiently large relative to the cost of remediation to justify tax-increment financing as a clean-up option.
  • An inverse distance-weighted average of faecal coliform counts taken at sampling stations suggest that the housing market is sensitive to such subtle environmental factors – Legget and Bockstael (2000).

Equity

Another interesting use of GIS in economics is investigating who benefits. These equity effects are often not thought about for CBA – do low-income households benefit from public infrastructure? Are people who have less choices adversely affected by the project? There has been some work done that allows us to answer many of the equity questions that are often outside of the scope of a traditional CBA.

  • Brainard et. al. (2003) examines the extent to which inequalities in noise exposure are present. Estimates of road and rail noise levels were made using established sound propagation models and were combined with data on noise generated from the city’s airport. Demographic details from the 1991 UK Census provided information on population age, ethic makeup, and deprivation. Disparities were observed in estimated noise exposures and levels of socio-economic deprivation.
  • The same authors, Brainard et. al. (2002), have studied air pollution. They find the overall distribution of the non-immigrant, native ethnic group (labelled as ‘>90% White’) enjoys substantially lower levels of CO pollution than does the Afro-Caribbean group. Allowing for variation in socio-economic circumstances, age distributions, household size, etc., Afro-Caribbean populations were still significantly more exposed to both CO and NO2 pollution then were other groups, including many of immigrant origin. This suggests that, while both non-immigrant and some immigrant groups are responding to improvements in personal socio-economic circumstances in similar ways (by relocating to lower-pollution neighborhoods), other groups are less responsive to such improvements.
  • (Pastor et al. 2001) looked at whether toxic waste dumps were sited in poor neighborhoods or whether low income people moved to where these sites were because of lower house prices. The finding was that disproportionate siting matters more than disproportionate move-in.

The way that this academic literature gets used in BIM models is through a distance decay function – people value an amenity (e.g. a park) or a dis-amenity (e.g. pollution source) less the further away they are from it. The WTP distance decay function will be the next post.

References

Bateman, I., A. Jones, A. Lovett, I. Lake and B. Day (2002) “Applying Geographical Information Systems (GIS) to Environmental and Resource Economics”. Environmental and Resource Economics 22: 219–269. (May 7th 2014)
Bateman, I. J., B. Day, I. Lake and A. A. Lovett (2001), The Effect of Road Traffic on Residential Property Values: A Literature Review and Hedonic Pricing Study. Edinburgh: Scottish Executive and The Stationery Office. Downloaded from: http://scotland.gov.uk/Resource/Doc/158818/0043124.pdf (May 12th 2014)
Belt, G. H., J. O’Laughlin and T. Merrill (1992), Design of Forest Riparian Buffer Strips for the Protection of Water Quality: Analysis of the Scientific Literature. Moscow, ID: University of Idaho. (May 12th 2014)
Bockstael, N. E. (1996), “Modelling Economics and Ecology: The Importance of a Spatial Perspective”, American Journal of Agricultural Economics 78 (December), 1168–1180. (May 12th 2014)
Brainard, J. S., A. A. Lovett and I. J. Bateman (1999), “Integrating Geographical Information Systems into Travel Cost Analysis and Benefit Transfer”, International Journal of Geographical Information Systems 13(3), 227–246. (May 12th 2014)
Brainard, J.S., Jones, A.P., Bateman, I.J., Lovett, and A.A., Fallon, P.J., (2002). “Modelling environmental equity: access to air quality in Birmingham UK.” Environment and Planning A, 34, pp. 695-716. Downloaded from http://www.envplan.com/abstract.cgi?id=a34184 (May 14th 2014)
Brainard, Julii S.; Jones, Andrew P.; Bateman, Ian J.; Lovett, Andrew A (2003), Modelling environmental equity: Exposure to environmental urban noise pollution in Birmingham, UK, CSERGE Working Paper EDM, No. 03-04 Downloaded from:http://www.econstor.eu/bitstream/10419/80295/1/367781328.pdf (May 12th 2014)
Carr, A. P. (1998), “Choctaw Eco-Industrial Park: An Ecological Approach to Industrial Land-Use Planning and Design”, Landscape and Urban Planning 42(2–4), 239–257. Access: http://www.sciencedirect.com/science/article/pii/S0169204698000905
Fleming, M. M (1999), “Growth Controls and Fragmented Suburban Development: The Effect on Land Values”, Geographic Information Sciences 15(2), 153–162. Downloaded from: http://www.iseis.cuhk.edu.hk/downloads/full_paper/1999-154-162.pdf (May 12th 2014)
Geoghegan, J., L. A. Wainger and N. E. Bockstael (1997), “Spatial Landscape Indices in a Hedonic Framework: An Ecological Economics Analysis Using GIS”, Ecological Economics 23, 251–264. (May 12th 2014)
Geoghegan, J. (2002), “The Value of Open Spaces in Residential Land Use”, Land Use Policy, in press. Access: http://www.sciencedirect.com/science/article/pii/S0264837701000400
Ihlanfeldt, K. R. and L. O. Taylor (2001), “Assessing the Impacts of Environmental Contamination on Commercial and Industrial Properties”. Florida: Department of Economics, Florida State University. Downloaded from: http://econweb.ucsd.edu/~carsonvs/papers/297.pdf (May 12th 2014)
Irwin, E. G. (2003), “Using GIS to Model Patterns of Urban-Rural Land Use Change”. Paper presented at Ohio Geospatial Technology Conference for Agriculture and Natural Resources , Columbus, Ohio March 24-26, 2003. Downloaded from: http://geospatial.osu.edu/conference/proceedings/papers/irwin_pap.pdf (May 12th 2014)
Kraak, M. J. and F. J. Ormeling (1996), Cartography: Visualisation of Spatial Data. Harlow: Longman.
Henry G. Overman (2006), “Geographical Information Systems (GIS) and Economics”, London School of Economics, 5th January 2006. Downloaded from: http://personal.lse.ac.uk/overman/research/GIS_and_economics_web.pdf (May 7th 2014)
Lake, I. R., A. A. Lovett, I. J. Bateman and B. Day (2000), “Using GIS and Large-Scale Digital Data to Implement Hedonic Pricing Studies”, International Journal of Geographical Information Systems 14(6), 521–541. Access: http://www.tandfonline.com/doi/abs/10.1080/136588100415729#.U3E7O3WW9CE
Leggett, C. G. and N. E. Bockstael (2000), “Evidence of the Effects of Water Quality on Residential Land Prices”, Journal of Environmental Economics and Management 39, 121–144. (May 12th 2014)
Mahan, B. L., S. Polaksy and R. Adams (2000), “Valuing Urban Wetlands: A Property Price Approach”, Land Economics 76(1), 100–113. Downloaded from: http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA326734 (May 12th 2014)
Orford, S. (1999), Valuing the Built Environment. Aldershot: Ashgate. Downloaded from: http://www.lundhumphries.com/pdf/tis/9780754610120_ROW.pdf (May 12th 2014)
Pastor, M., J. Sadd and J. Hipp (2001), “Which Came First? Toxic Facilities, Minority Move-In, and Environmental Justice?”, Journal of Urban Affairs 23(1), 1–21. (May 12th 2014)
Powe, N. A., G. D. Garrod, C. F. Brunsdon and K. G.Willis (1997), “Using a Geographic Information System to Estimate an Hedonic Price Model of the Benefits of Woodland Access”, Forestry 70(2), 139–149. Downloaded from: http://forestry.oxfordjournals.org/content/70/2/139.full.pdf (May 12th 2014)
Rhind, D. W. (1990), “Global Databases and GIS”, in M. J. Foster and P. J. Shand, eds., The Association for Geographical Information Yearbook 1990. London: Taylor and Francis.

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