USING GIS ANALYSIS TO TARGET EDUCATIONAL PROGRAMS FOR PRIVATE WELL

OWNERS

By

JAIMIE HOLMES

Photo Credit: Ayla Fox

A MAJOR PAPER SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF MASTER OF ENVIRONMENTAL SCIENCE AND MANAGEMENT

UNIVERSITY OF RHODE ISLAND

April 21, 2017

MAJOR PAPER ADVISOR: Dr. Arthur Gold

MESM TRACK: Wetlands, Watersheds, and Ecosystem Science

Holmes | 1

Abstract

Cooperative Extension programs typically collect basic participant information such as phone

number, and street, mail, and email addresses. The University of Rhode Island’s Cooperative

Extension (URI CE) Water Quality Program has begun to use some of this information to

enhance program development and effectiveness. URI CE uses Geographic Information Systems

(GIS) in a variety of ways to improve its program for private well owners. Using GIS, URI CE

has targeted potential “hot spots” of well water contamination to focus outreach and technical

assistance. This paper outlines an additional GIS method used to improve locations for private

well workshops in order to achieve program outcomes utilizing routinely collected basic

participant information.

Background

Cooperative Extension (CE) has long been tasked with extending the knowledge base of the

Land Grant Universities to community members to help them take action that improves their

lives (Arnold, Hill, Bailey, & Meyers, 2012). CE provides a multitude of programs on topics

such as agriculture, nutrition, natural history, and water quality. The University of Rhode

Island’s Cooperative Extension (URI CE) Water Quality Program provides educational and

technical assistance to private well owners in a variety of ways including: single-session place-

based workshops; participating in community fairs, festivals, events; distribution of printed

materials via website and social media; and, interaction via phone, email and face-to face.

Single-session workshops are the cornerstone to deliver private well education and technical

assistance to the state’s private well owners (McCann & Gold, 2012).

About 15% of Rhode Island’s population relies on private wells for their drinking water. Private

well owners are responsible for testing and protecting their drinking water and many are unaware

of what this entails (Fox, Nachman, Anderson, Lam, & Resnick, 2016). URI CE is instrumental

in closing this knowledge gap.

Single-session workshops are a proven effective method of delivering important information to a

public audience that result in behavior change (McCann & Gold, 2012). Workshop outcomes

include behavior change such as regular well water quality testing, proper disposal of household

hazardous waste, regular septic system maintenance, and use of proper lawn and garden care

practices. These behavior changes ultimately lead to drinking water quality protection for the

private well owner and local groundwater quality in general. Attracting the target audience to a

community workshop relies on use of convenient, deliberate venues. This paper describes the use

of a multi-criteria analysis that targets locations for private well workshops to realize program

outcomes.

Holmes | 2

Figure 1. Map

developed using

GIS depicting

private well areas

of RI.

Introduction

Rhode Island Geographic Information System (RIGIS) maintains a database of publically

available GIS data at www.rigis.org. RIGIS contains a detailed statewide database that spans a

variety of subjects, from municipal boundaries and water supply districts, to conservation land

and infrastructure data. The data made available by RIGIS can be utilized in many types of GIS

analyses.

Spatial multi-criteria decision analysis is defined by Rikalovic et al. (2013) as “a process that

combines and transforms geographical data into a resultant decision”. By using this method, they

could develop candidate sites, evaluation criteria, and constraints that were analyzed using GIS

to determine the best possible location for new industrial development. A similar method is used

by GIS-users today for habitat suitability modeling, siting rural buildings, and siting potential

wind farms (Hansen, 2005; Jeong, Garcia-Moruno, & Hernandez-Blanco, 2012; Store & Kangas,

2001).

Implementation of similar methods has provided priority areas for conservation throughout

Rhode Island. A model developed to analyze five different resource themes including

groundwater, surface water, biodiversity, agriculture, and cultural, recreational, or aesthetic

resources was used to provide land trusts with guidance as to what parcels of land held the

highest conservation value (McCann, Chapman, & Mandeville, 2001).

Buffer analysis using GIS can capture features within certain distances of target features. Use of

this method was implemented to determine land-use types, wetland area, and road density within

1 km of North American Amphibian Monitoring Program data collection points that was in turn

used to determine the negative effects of roads on amphibian distributions (Cosentino, et al.,

2014).

3 GIS Approaches to Enhance URI CE Program Effectiveness for Private Well Owners

1. In 2000, URI CE began targeting appropriate

audiences for private well workshops using

geographic information systems (GIS) to

determine areas in the state that rely on private

wells (Figure 1). By overlaying public water

supply districts and towns data layers, we used the

Erase tool to remove areas serviced by public

water supply districts in Rhode Island. The

resulting shapefile shows areas serviced by private

wells. URI CE now holds private

well workshops within these

areas to make the workshops

convenient for well owners.

Holmes | 3

Figure 2. Map developed using GIS

depicting potential “hot spots” for nitrate-

nitrogen contamination.

2. Through partnership with the Rhode Island Department of Health (RIDOH), nitrate-

nitrogen contamination, which can have adverse health effects, was discovered in a

private well area on Aquidneck Island. RIDOH has regulations requiring private well

testing at the sale or transfer of property (Rhode Island Department of Health, 2008). The

well test results of a home for sale in Middletown, RI showed a nitrate-nitrogen

concentration of 27.1 parts per million (ppm); the maximum contaminant level (MCL) set

by the Environmental Protection Agency (EPA) for drinking water is 10ppm. This result

prompted RIDOH to notify neighboring private well owners.

More well tests from surrounding homes resulted in elevated nitrate-nitrogen

concentrations, prompting concern amongst town officials and residents. RIDOH and

URI CE responded with outreach, education, and technical assistance to the community

and its residents about testing, protection and subsequent water quality treatment.

Because of the Middletown experience, RIDOH and URI CE proactively developed a

GIS approach to identify areas throughout Rhode Island that could be at-risk for nitrate-

nitrogen contamination based on surrounding land uses. Areas identified were 1) serviced

by private wells, 2) near agricultural land, and 3) where RIDOH had public and private

well testing results showing elevated nitrate-nitrogen concentrations (Figure 2). These

“hot spots” then received targeted outreach, education, and technical assistance to

promote well testing and drinking water protection.

This “hot spot” analysis highlighted areas in

Hopkinton, Glocester, North Smithfield, Little

Compton and several other towns for potential

nitrate-nitrogen contamination to groundwater.

Cross-referencing these areas with past private

well workshops showed little outreach had

taken place in North Smithfield, RI in recent

years. To reach this audience of potentially

affected private well owners, a workshop was

held at the North Smithfield Public Library, in

September 2014, a few months after

completing the “hot spot” analysis. The

workshop attracted about 30 attendees and

communicated the issues of potential nitrate-

nitrogen contamination.

Following this workshop, a number of private

well water samples were submitted to RIDOH

through URI CE’s facilitated testing; 25% were

found to have nitrate-nitrogen levels above the

Holmes | 4

5ppm action level. A state record nitrate-nitrogen result of 38.4ppm was established

through well testing at this workshop. The “hot spot” analysis continues to be used today

when prioritizing target areas to hold workshops. With these areas of potential nitrate-

nitrogen contamination identified, URI CE and RIDOH have addressed the importance of

annual testing in several towns across the state.

3. CE programs typically collect basic participant information such as phone number and

street, mail, and email addresses. URI CE has begun to use some of this information to

enhance program development and effectiveness.

Use of GIS to determine workshop attendees’ travel patterns can enhance the program’s

ability to target specific audiences. By analyzing spatial patterns between physical

addresses of workshop attendees’ and locations we can determine how far they are

travelling to attend workshops. If we know what distance most participants are travelling

and a private well water issue, such as nitrate-nitrogen contamination, arises in a specific

neighborhood we can look within that distance for a convenient venue to attract our target

audience.

The following method uses ArcGIS 10.3.1 for Desktop from ESRI (Environmental

Systems Research Institute) to determine if a spatial pattern between physical addresses

of attendees’ and workshop locations exists.

Methods

Software Preparation

Connect local ArcGIS to ArcGIS server maintained by the Environmental Data Center (EDC) at

the University of Rhode Island (URI) (Figure 3).

Figure 3

Holmes | 5

Use the EDC server to access the address locator maintained by RIGIS (Figure 4).

Figure 4

Data Preparation

Download Rhode Island state boundary shapefile from RIGIS (Figure 5).

Figure 5

Holmes | 6

Compile address data for workshop locations and attendees from 2004-2016 (Figure 6). Attendee

address data from 2011-2012 is missing due to a server backup issue. Block Island and Osher

Lifelong Learning Institute (OLLI) attendee address data were omitted for several reasons

including travel restrictions, the small size of Block Island, and outside incentives for attendance

at the OLLI workshop.

Figure 6

Holmes | 7

Using data from 72 workshops covering 32 different workshop locations, organize excel

spreadsheets of addresses in proper format for ArcGIS to accurately geocode (Figure 7),

including shortened column headers with no space, correctly formatted zip codes with

concatenated zeros (Figure 8).

Figure 7

Figure 8

Save as .csv files.

Holmes | 8

Data Upload to ArcGIS - Geocoding Workshop Locations

Geocode addresses from .csv file (Figure 9).

Figure 9

Choose ‘Single Field’ as the Address Input Fields and the column header of the addresses as the

Single Line Input (Figure 10). Click on ‘Advanced Geometry Options…’ and choose ‘Use the

map’s spatial reference’ (Figure 11).

Figure 10 (left) & Figure 11 (right)

Holmes | 9

Click ‘OK’ and wait for geocoding to finish, the result will show the number of addresses that

were successfully matched, tied, or unmatched (Figure 12).

Figure 12

Click ‘Rematch’ to view all potential matches and to manually rematch any tied or unmatched

addresses (Figure 13).

Figure 13

Holmes | 10

Use Identify tool to manually check all matched points (Figure 14).

Figure 14

Export data as shapefile and change symbology (Figure 15).

Figure 15

Holmes | 11

Use Multiple Ring Buffer tool to buffer each location 5, 10, 15, 20, 150 miles (Figure 16); the

150 mile buffer is set to ensure coverage of the entire state (Figure 17).

Figure 16

Figure 17

Holmes | 12

Open attribute table of the multiple ring buffer output file and sort the buffers by workshop

location (Figure 18).

Figure 18

Select the 5 buffers per workshop location and create a new shapefile from the selection (Figure

19).

Figure 19

Holmes | 13

Clip new layer with Rhode Island boundary (Figure 20).

Figure 20

Export shapefile of clipped buffers, rename and change symbology of exported shapefiles

(Figure 21).

Figure 21

Holmes | 14

Data Upload to ArcGIS - Geocoding Attendee Addresses

Geocode addresses from .csv file (Figure 9).

Choose ‘Single Field’ as the Address Input Fields and the column header of the addresses as the

Single Line Input (Figure 10). Click on ‘Advanced Geometry Options…’ and choose ‘Use the

map’s spatial reference’ (Figure 11).

Click ‘OK’ and wait for geocoding to finish, the result will show the number of addresses that

were successfully matched, tied, or unmatched (Figure 12).

Click ‘Rematch’ to view all potential matches and to manually rematch any tied or unmatched

addresses (Figure 13).

Cross-reference tied and unmatched addresses with tax assessor’s online database (Figure 22)

and Google Maps.

Figure 22

Holmes | 15

Finalize address matching and display matched points (Figure 23).

Figure 23

Use Identify tool (Figure 14) to randomly recheck 5 matched points per town and all points that

were matched with addresses outside of the private well areas of Rhode Island.

Export data as shapefile.

Holmes | 16

Data Compilation & Analysis - Spatial Join

Use Spatial Join tool to overlay attendee address points and buffer polygons to create new file

with the original data from each point as well as data from every buffer layer that intersects each

point (Figure 24).

Figure 24

Use the Select by Attribute tool to query data points from one workshop within each buffer

beginning with the 150-mile buffer (Figure 25). Display the correct workshop location buffers as

a visual aid. This selection provides the total number of attendees per workshop.

Figure 25

Recheck any outliers (points falling outside of 15-mile buffer - red or orange buffer) using the

Identify tool (Figure 14); eight errors were discovered this way. Four were rematched correctly

and four were unable to be rematched due to missing data in the RIGIS address locator file.

Continue using the Select by Attribute tool to query data points from within the 20-mile buffer .

Display the correct workshop location buffers as a visual aid. This selection provides the total

Holmes | 17

number of attendees within 20 miles of the workshop location. The difference between this

selection and the 150-mile buffer selection provides the number of attendees travelling from a

distance greater than 20 miles (Table 1).

Continue using the Select by Attribute tool to query data points of each buffer until completed

for all buffers. The total number of participants within each buffer indicates distance travelled as

summarized in Table 1.

Table 1. Basic calculations used to determine number of workshop attendees travelling from

within each buffer

Basic Calculation # of workshop attendees per buffer

# of attendees selected within 150-mile buffer Total # of workshop attendees

# of attendees selected within 150-mile buffer

minus # of attendees selected within 20-mile

buffer

# of attendees travelling more than 20 miles

# of attendees selected within 20-mile buffer

minus # of attendees selected within 15-mile

buffer

# of attendees travelling between 15 and 20

miles

# of attendees selected within 15-mile buffer

minus # of attendees selected within 10-mile

buffer

# of attendees travelling between 10 and 15

miles

# of attendees selected within 10-mile buffer

minus # of attendees selected within 5-mile

buffer

# of attendees travelling between 5 and 10

miles

# of attendees selected within 5-mile buffer # of attendees travelling less than 5 miles

Holmes | 18

Data Recording

Use Microsoft Excel to record the number of attendee data points from each workshop falling

within each buffer (Figure 26).

Figure 26

Calculate percentages of attendees travelling from within each buffer range.

Results

Our workshop registration records contained 1,289 attendee addresses, about 1,050 were initially

matched using the Geocode Addresses tool (Figure 9). Another 219 addresses were correctly

matched after comparison with the tax assessor’s online database or Google maps (Figure 22). A

total of 20 addresses were unmatched due to missing data in the RIGIS geolocation file.

The analysis showed over 64% of attendees travel less than 5 miles to participate in private well

workshops in Rhode Island. Over 90% of workshop attendees travel up to 10 miles (Table 2).

Table 2. Percent of attendees and their travel distances

Buffer Distance (miles)

0-5 5-10 10-15 15-20 >20

Percentage of

attendees

64.78% 26.00% 7.09% 1.34% 0.79%

Holmes | 19

Of the 32 workshop venues analyzed, 23 venues attracted most attendees from within 5 miles. A

majority of attendees travelled between 5 and 10 miles to 8 of the workshop venues. One

workshop venue attracted most attendees from less than 5 miles and between 5 and 10 miles

equally (Table 3).

Table 3. Percent of attendees and their travel distances based on majority

Number of

Workshop

Venues

Buffer Distance (miles)

0-5

5-10

10-15

15-20

>20

23 venues 73.93% 18.64% 5.76% 1.15% 0.52%

8 venues 36.73% 48.98% 10.54% 2.04% 1.70%

1 venue 40.00% 40.00% 20.00% 0.00% 0.00%

Discussion

Results of this analysis has helped program staff realize a spatial pattern exists between

workshop locations and attendees’ proximity to these locations. Just as GIS can be used to site

suitable wind turbine locations or prioritize conservation lands, we have found that GIS can be

used to site suitable workshop locations for URI CE private well water educational programs.

The program already used some routinely collected information from workshop attendees for:

● Assessing program impacts (mailing address) - An annual survey is sent to

each workshop participant to determine what they have done as a result of

attending the workshop.

● Social marketing and email newsletter (email address) - An e-newsletter is

sent out four times each year to our database of email addresses collected from

workshop participants and other interested individuals.

Although they make up large portion of the participant database, prior to this spatial analysis

physical addresses had not been used to inform workshop location selection to improve audience

targeting.

Combination of these results, input from RIDOH, and knowledge of potential groundwater issues

from either anthropogenic or naturally-occurring substances, can help target specific

neighborhoods that may require education and technical assistance. URI CE can find a venue

within 10 miles of the area of interest to host the workshop and attract the target audience. We

acknowledge that our results indicate a majority of participants travel within 5 miles and we try

Holmes | 20

to secure venues as close to target areas as possible. However, challenges may arise when

scheduling workshops in rural communities. In these communities we may have to go as far as

10 miles to secure a suitable venue that provides the necessary amenities and has the capacity to

hold our expected number of participants.

Before beginning a similar analysis, it is important to consider potential barriers such as time,

data, or knowledge constraints. Four main constraints were established throughout our analysis.

Potential Constraints

1. Time – Significant time was allotted for data preparation to ensure proper formatting of

Excel files before making any attempt to geocode the addresses. Despite the time spent,

issues arose during each phase of geocoding. Time was spent consulting with EDC staff

at URI and ultimately issues using the geocoding tool were resolved.

2. Data Accessibility – This process was simplified by the development and maintenance of

the address locator file by the EDC, without this file the geocoding process would have

taken considerably more time and effort. The public access of data through the RIGIS

website also contributed to easing this process.

3. Project Specific Database – URI CE’s database is maintained on a regular basis by

graduate and undergraduate students working for the program for 6 months – 2 year

periods of time. Given the frequent turnover of student staff it can be difficult to maintain

consistent formatting and monitoring of data for spelling mistakes. The original data were

sorted and checked for obvious mistakes in spelling and formatting which again took a

considerable amount of time.

4. GIS Knowledge – An intermediate-advanced understanding of GIS is necessary to

complete this analysis and navigate the hurdles throughout the process.

Conclusion

URI CE has found this to be a valuable tool in assisting us in choosing workshop locations. In

addition, we use this analysis, coupled with the “hot spot” analysis to target specific

neighborhood locations. Following the “hot spot” analysis URI CE and RIDOH noticed an area

of Hopkinton where there is potential for nitrate-nitrogen contamination. We worked closely

with the community to host a private well workshop at the Hopkinton Town Hall, which was

close to the identified “hot spot”. In an effort to attract the target audience, Hopkinton Town Hall

was chosen over Chariho Middle School, a previous workshop location in the same region of

Rhode Island, because it was close to the “hot spot” identified.

Additionally, these results highlight the need to rotate venues when offering the same program in

the same towns over successive years. URI CE has been offering community workshops for over

Holmes | 21

13 years. We have begun to change venues in certain areas. A recent workshop was held at

Ponagansett High School rather than the North Scituate Community House, which has been a

venue for many of our workshops in that region of Rhode Island.

References

Arnold, S., Hill, A., Bailey, N., & Meyers, C. (2012). Extension's Online Presence: Are Land-Grant

Universities Promoting the Tripartite Mission? Journal of Extension.

Cosentino, B. J., Marsh, D. M., Jones, K. S., Apodaca, J. J., Bates, C., Beach, J., . . . Willey, A. (2014).

Citizen science reveals widespread negative effects of roads on amphibian distributions.

Biological Conservation, 31-38.

Fox, M. A., Nachman, K. E., Anderson, B., Lam, J., & Resnick, B. (2016). Meeting the public health

challenge of protecting private wells: Proceedings and recommendatiosn from an expert panel

workshop. Science of the Total Environment, 113-118.

Hansen, H. S. (2005). GIS-based Multi-Criteria Analysis of Wind Farm Development. ScanGis, 75-87.

Jeong, J. S., Garcia-Moruno, L., & Hernandez-Blanco, J. (2012). Integrating buildings into a rural

landscape using a multi-criteria spatial decision analysis in GIS-enabled web environment.

Biosystems Engineering, 82-92.

McCann, A., & DiPippo, P. (2016). Rhode Island Well Owners Take Action to Protect Their Drinking

Water. Proceedings of the American Water Works Association, Sustainable Water Management

Conference. Providence, Rhode Island.

McCann, A., & Gold, A. J. (2012). Engendering Behavior Change Through Single-Session Workshops:

Lessons Learned from Extension's Private Well Initiative. Journal of Extension.

McCann, A., Chapman, D., & Mandeville, A. (2001, Summer). Using GIS to Identify Critical Lands for

Conservation. Maritimes, pp. 10-12.

Rhode Island Department of Health. (2008). Rules and Regulations Pertaining to Private Drinking Water

Systems. Providence.

Rikalovic, A., Cosic, I., & Lazarevic, D. (2013). GIS based multi-criteria analysis for industrial site

selection. Procedia Engineering, 1054-1063.

Store, R., & Kangas, J. (2001). Integrating spatial multi-criteria evaluation and expert knowledge for GIS-

based habitat suitability modelling. Landscape and Urban Planning, 79-93.