Location Location Location: Residential Distributions on the Shorelines of North Pond and East Pond

By Sophie Sarkar’11

Colby College Environmental Studies Program

GIS and Remote Sensing

ABSTRACT

 In this study I examine the spatial distribution of high value and new residences on the shoreline of East Pond and North Pond. To do so, I analyzed property tax data in ArcGIS, between lakes and within lakes, to determine whether there was spatial clustering of the following shoreline property characteristics: property values, price/meter² of land, and house age. The results identify six potential areas between the two lakes with significantly higher property values, land values, or house ages. If these variables are effective proxies for household income and resident age, then the identified areas may be good locations for local lake associations to target stakeholder engagement projects.

INTRODUCTION

One of the main challenges that local lake conservation organizations on the Belgrade lakes face is engaging residents in lake stewardship (Shannon 2010). Several studies on pro-environmental behavior have shown that certain demographic characteristics are significant determinants of whether an individual is willing to pay for and participate in environmental stewardship. These studies have shown a positive relationship between income and conservation behavior and a negative relationship between age and conservation behavior (Del Saz-Salazar et al. 2009; Steg and Vlek 2009; Fransson and Garling 1999). A recent contingent valuation survey of shoreline residents on North Pond and East Pond that I conducted in March confirmed these findings for the region. Using two of the Belgrade Lakes, East Pond and North Pond, as case studies, I attempt to locate concentrations of high land values, property values, and house age as proxies for income and resident age in an effort to help local conservation groups gain financial support for their conservation programs.

METHODS

 I constructed a database by joining per parcel tax data from the Belgrade Regional Conservation Alliance with spatial data of East Pond and North Pond’s shoreline lots (McCullough 2010). The tax data included information on land and property value, while the spatial data included information on lot size, location, and the year each residence was built. Then, using ArcMap 9.3, I created a point layer out of the shoreline lot polygons, from which I generated four kernel density maps. From these maps, I selected out lots within concentrated areas of high land values (measured as price/m²), property values, and low age. I labeled these areas A-F (Figure 4), and conducted a non-parametric analysis, using the Mann-Whitney U test, to see whether these regions had significantly higher land and property values and significantly lower ages compared to the full shoreline population. North Pond and East Pond were analyzed separately.

RESULTS

In the results that follow, averages are reported in parentheses. On North Pond, Areas B ($83.13/m²), C ($82.92/m²), and D ($77.89) have significantly higher land values than the rest of the shoreline population. Area B ($112,016), however, also has significantly lower property values. The results of the analysis of house age show that Area A has significantly newer houses (18 years) and Areas C (74 years) and D (57 years) have significantly older houses. Table 1 summarizes these results. Within the East Pond shoreline population, both Areas E ($70.67/m²) and F ($20.14/m²) are concentrated with significantly higher land values than the rest of the shoreline population. Area E is also characterized by significantly higher property values ($165,808). On the other hand, Area F has significantly lower property values ($67, 814). The results of the analysis of house age show that Area E has significantly older houses (66 years) and Area F has significantly newer houses (38 years). Table 2 summarizes these results.

Figure 1. A map showing concentrations of high land values along East Pond and North Pond

Figure 2. A map showing concentrations of high property values along East Pond and North Pond

Figure 3. A map showing concentrations of new houses along East Pond and North Pond

Figure 4. A map showing the five areas analyzed based on the kernel density layers above

Table 2: A Comparison of land values, property values, and house age between the highlighted areas E and F and the rest of the East Pond shoreline

Location on East Pond Shoreline	Price/m^2			Property Value			Age of House
	N	Mean	Mann- Whitney (P-value)	N	Mean	Mann-Whitney (P-value)	N	Mean	Mann-Whitney (P-value)
East Pond (E)
EP-E	13	70.67	-5.389	13	165808	-2.886	13	66	3.242
Rest of Shoreline	213	20.14	(<0.01)**	213	142528	(0.004)**	213	43	(0.001)**
East Pond (F)
EP-F	85	27	8.050	85	67814	8.094	85	38	-3.861
Rest of Shoreline	141	20.89	(<0.01)**	141	189297	(<0.01)**	141	48	(<0.01)**

(*)p<0.05; (**)p<0.01

Table 1: A Comparison of land values, property values, and house age between the highlighted areas A, B, C, D and the rest of the North Pond shoreline

Location on North Pond Shoreline	Price/m^2			Property Value			Age of House
	N	Mean	Mann- Whitney (P-value)	N	Mean	Mann-Whitney (P-value)	N	Mean	Mann-Whitney (P-value)
North Pond (A)
NP-A	44	61.79	-1.485	44	140186	0.677	44	18	-5
Rest of Shoreline	206	64.96	(0.6277)	206	144824	(0.499)	206	51	(p<0.01)**
North Pond (B)
NP-B	31	83.13	-2.872	31	112016	2.876	31	52	1.213
Rest of Shoreline	219	61.75	(0.004)**	219	148498	(0.004)**	219	46	(0.225)
North Pond (C)
NP-C	38	82.92	-2.923	38	139971	-0.683	38	74	5.785
Rest of Shoreline	212	59.30	(0.004)**	212	144710	(0.495)	212	42	(p<0.01)**
North Pond (D)
NP-D	29	77.89	-2.581	29	146100	-1.465	29	57	2.289
Rest of Shoreline	221	62.62	(0.010)**	221	143717	(0.143)	221	45	(0.0221)*

(*)p<0.05; (**)p<0.01

DISSCUSSION

Assuming that both property and land values are good proxies for household income, and holding age constant, Area E on East Pond is probably the best target for conservation groups looking to find financial assistance for their projects. If land values alone are taken into consideration, then Areas B, C, and D on North Pond, and Areas E and F on East Pond may also be good locations to focus their residential engagement campaigns. Lastly, assuming that house age is a good proxy for resident age, and that younger residents are more willing to pay for and participate in conservation (Del Saz-Salazar et al. 2009; Steg and Vlek 2009; Fransson and Garling 1999), Area A on North Pond and Area F on East Pond would be the best locations to target engagement campaigns. The results of this study, however, are difficult to interpret for several reasons. The first is that the two different proxies used for household income were often negatively associated, and it is unclear which is a better proxy for household income. Additionally, in many areas the house age variable was positively associated with higher property and land values, and thus it remains unclear whether residents in those areas would be more likely or less likely to engage in lake stewardship. Furthermore, it is difficult to say whether house age is a good proxy for resident age without more information about household residents.

CONCLUSION

Although there do seem to be concentrated areas of high property values, high land values, and older houses on both North Pond and East Pond, it is not clear whether these variables are sufficient proxies for household income and resident age. Therefore, the results of this study are largely inconclusive. However, the structure and the methodology behind this study can be used as a model for future research with survey data that reveals more information about the residents on each lot, including household income and age.

ACKNOWLEDGEMENTS

I would like to thank Professor Philip Nyhus for the many GIS lab hours he devoted to all of his students this semester. I would also like to thank Manny Gimond for his enormous GIS wisdom, kindness, and patience.

Works Cited

Bouchard, Roy. 2010. Maine DEP Lake Assessment Program.

Del Saz-Salazar, Salvador, Francesc Hernandez-Sancho, and Ramun Sala-Garrido. 2009. The social benefits of restoring water quality in the context of the Water Framework Directive: A comparison of willingness to pay and willingness to accept. Science of The Total Environment 407 (16):4574-4583.

Fransson, N., and T. Garling. 1999. Environmental concern: conceptual definitions, measurements, methods, and research findings. Journal of Environmental Psychology 19 (369-382)

McCullough, Ian 2010. The impacts of land use and development patterns on water quality of the Belgrade Lakes. Environmetnal Studies Program. Colby College, Waterville, ME.

Michael, Holly, Kevin Boyle, and Roy Bouchard. 1996. Water Quality Affects Property Prices: A Case Study of Selected Maine Lakes. Maine Agricultural and Forest Experiment Station Misc. Report 398, Feb 1996, Univ. of Maine

MDEP 2010. Draft 2010 Integrated Water Quality Assessment Report. Maine Department of Environmental Protection.

Shannon, Maggie. 2010. Maine Congress of Lake Associations.

Steg, Linda, and Charles Vlek. 2009. Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology 29 (3):309-317.