A Public Participatory Approach toward the Development of a

Comprehensive Geospatial Database in Support of High-scale Food

Security Analysis

Timothy Mulrooney

and Tysean Wooten

Department of Environmental, Earth and Geospatial Sciences, North Carolina Central University,

1800 N. Fayetteville Street, Durham, NC, U.S.A.

Keywords: Geographic Information System, Geodatabase Development, Geospatial Standards, Geospatial Metadata,

Geospatial Data Development, Food Desert, Food Swamp.

Abstract: While Geographic Information Systems (GIS) has slowly been integrated into the study of the food

environment, little research has been performed to determine the data development needs and standards that

best necessitate high-quality research at a high scale. In an era with limited resources such as personnel,

bandwidth, space and time, the optimization of these resources in order to understand, visualize and facilitate

interventions at an appropriate scale is critical if not necessary. In this research, subject matter experts assessed

and evaluated the relative importance of various GIS data themes, attributes and facets of GIS database

development in support of local-scale food security analysis. It was found that factors related to the placement

of various food sources (grocery stores and farmers markets) and individualized vehicular transportation

(roads) outweighed those related to land cover, utilities and zoning, as well as non-vehicular (sidewalks) and

public (bus routes) means of transportation. In addition, when ranking various dimensions of data quality,

subject matter experts found positional accuracy and attribute accuracy to be the most important when

undertaking the development of a geospatial database of this magnitude.

1 INTRODUCTION

Patterns of negative health-related outcomes such as

obesity, hypertension, and diabetes are spatial in

nature and when mapped, are typically prevalent and

clustered in low-income communities. While lifestyle

choices and genetics contribute to individual and

household vulnerability that lead to these differential

health outcomes, it is possible to identify social and

environmental factors, sometimes associated with

geographic location, that have an effect on larger

groups, and might be considered as critical indicators

to address in any mitigation plan. There is, for

example, a strong relationship between health and

diet and it seems clear the accessibility of sources for

fresh meats, fruits, and vegetables is an important

factor in the overall health of a community. Even in

poorer neighborhoods, Rose and Richards (2004)

found food stamp recipients who live close to

https://orcid.org/0000-0001-9333-9641

https://orcid.org/0000-0001-8355-3249

supermarkets ate more fresh food and vegetables.

While it is safe to say that geography is not a prime

determinant in explaining or even justifying health

outcomes, it does have more of a role than one would

think.

The United States Department of Agriculture

(USDA) has popularized the term food desert to

highlight areas within low-income communities that

have limited accessibility to supermarkets. While

some research has focused on rural areas (Van

Hoesen, 2013; Gross and Rosenberger, 2005;

Blanchard and Lyson, 2006; Morton, Ella and

Oakland, 2005) much of the knowledge base on the

subject has been associated with urban areas. In

urban areas, this phenomenon can occur for a couple

of reasons. The number of large retailers is decreasing

or consolidating, but increasing in size to

accommodate all shoppers, both grocery and non-

grocery (Clarke et al., 2002). Combined with the fact

that retailers are leaving downtowns for the suburbs

Mulrooney, T. and Wooten, T.

A Public Participatory Approach toward the Development of a Comprehensive Geospatial Database in Support of High-scale Food Security Analysis.

DOI: 10.5220/0008863900210032

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 21-32

ISBN: 978-989-758-425-1

(Furey et al., 2001), Mamen (2007) found large

retailers are tending to locate near high-volume roads

that are less accessible to non-vehicular

individualized transportation (i.e. walking, public

transit or riding a bike). Lewis et al. (2005) reinforced

this when he found unhealthy food options greatly

outweighed their healthy counterparts in Los Angeles

while Powell (2007) found poor and minority

neighborhoods had less healthy food options than

their richer and whiter counterparts. As a result,

typical sources of fresh and ‘healthy’ foods

(supermarkets, farmers’ markets and other sources)

are being replaced by fast food restaurants and

convenience stores, which offer food options that are

convenient, inexpensive but typically less healthy.

While a seemingly even trade in terms of net food

balance, the long-term ramifications on community

health far outweigh any gains. In response to this

increasing disproportion, research has explored the

notion of food swamps which represent areas with

inordinately high number of unhealthy food options

compared to healthy options. Research at the local

level (Cooksey-Stowers et al., 2017; Zenk et al.,

2015) has shown food swamps actually better predict

obesity and other negative health outcomes than food

deserts.

Geospatial tools such as Geographic Information

Systems (GIS) serve as a popular technology to assess

and evaluate spatial dimensions of the food

environment. A GIS serves as the tangible and

intangible means by which information about

spatially-related phenomena can be created, stored,

analyzed and rendered in the digital environment.

Experts in many dissimilar fields have seen the utility

of GIS as a means of quantifying and expanding their

research. GIS is used in disciplines such as business,

sociology, justice studies, surveying and the

environmental sciences. As applied to food security,

GIS can be used to measure the proximity of

residences to large supermarkets or supercenters or the

concentration of food outlets within an enumeration

unit (census tract or zip code) as a commonly used

proxy for access (Morton and Blanchard, 2007;

Sharkey and Horel, 2008). These areas of high access

and low access can be analyzed and mapped across

both space and time (Chen and Clark, 2013) as shown

in Figure 1, as well as the factors that may explain this

access such as spending (Figure 2). These make

powerful visual products both easy to understand and

disseminatable to the entire public that can have long-

term policy implications.

Figure 1: Map of USDA food deserts in Guilford County,

North Carolina.

Figure 2: Map of spending patterns combined with food

deserts.

While many only see the output of GIS data and

analysis in the form of maps, resources must be

dedicated to creating high-quality data at a local scale.

This data creation takes on many different forms,

ranging from the conversion of analog data and

extraction from a larger database to the use of high

precision equipment. This paper takes a holistic look

at the types of geospatial data needed to perform high-

quality analysis in support of assessing, evaluating

and mapping spatial dimensions of the food

environment at a local scale. These database needs

are quite different than data that may be required to

remediate food insecurity at the individual/household

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

level or at a national or sub-national scale. Minimal

research has been performed in this field of database

development, whether for the sake of science

research, decision-making or policy.

In the United States, food insecurity has been

described as a “serious public health problem

associated with poor cognitive and emotional

development in children and with depression and

poor health in adults” (Chilton and Rose, 2009, p. 1).

Some have called for a rights-based approach to

addressing food security in the United States given

that women and children have much higher rates of

food security than their male and more senior

counterparts (Chilton and Rose, 2009). As a result,

this research explores both technical and non-

technical issues by understanding the needs and

subsequently developing the database to solve this

pressing and immediate problem.

2 LITERATURE REVIEW

While many operational definitions exist, food

security is generally considered to be the state “when

all people, at all times, have physical and economic

access to sufficient, safe and nutritious food to meet

their dietary needs and food preferences for an active

and healthy life” (Food and Agriculture Organization

2009, p. 1). Contemporary literature has used terms

such as availability, accessibility, proximity,

disparity, inequality, density, variety, affordability

and quality as well as the aforementioned food desert

and food swamp to describe quantitative measures of

the food environment and ultimately food security.

These measures, as well as the data which describe

them, can be represented at different scales. The data

needs for national-level food security analysis differs

than those required for community level analysis.

The mapping and delineation of food-insecure

areas within the digital environment has been made

exponentially easier with GIS technologies. While

first used as an aesthetic tool to map study areas

(Wrigley et. al, 2002) or display underlying

explanatory variables (Guy et. al, 2004), GIS has

since been used to measure distances,

quantitatively

express proximity and render this proximity with

statistical significance using a

variety of analytical,

geostatistical and cartographic techniques. Among

the

first to do this within the realm of food desert

research were Donkin et al. (1999), Lovett et al.

(2002) and Pearce (2006) while more recent research

(Mulrooney, 2017; Rose et al., 2009) has

quantitatively calculated and mapped the spatial

extent of the aforementioned food swamps at a local

scale.

Within the GIS data environment, ways to express

quantitative dimensions of the food environment vary

from study to study. Prior research has expressed

these measures as absolute linear units such as

kilometers or miles (Jago, 2007), travel time in

minutes (Ver Ploeg et al., 2009; Jiao et al., 2012) and

densities such as the number of food options per

square mile by census tract (Block et al., 2004), as

well as derived metrics based on the cost to operate a

car (Hallett and McDermott, 2011). More recently,

relative unitless metrics (Zenk et al., 2014; Clary et

al., 2015; Mason et al., 2013) have been used as

alternatives to absolute measures because these

absolute measures are meaningless if not placed

within some context. A ten-minute drive time to the

nearest fresh food source in a downtown urban area

means something much different than a ten-minute

drive to the nearest fresh food source in a rural area.

The proper and prudent use of absolute measures

requires more data, analysis and interpretation. Food

swamp research using GIS has used existing metrics

such as the Retail Food Environmental Index (RFEI)

and the Expanded RFEI (Cooksey-Stowers et al.,

2017; Luan et al., 2015) while others (Mulrooney et

al., 2017; Rose et al, 2009) have derived their own

metrics and subsequent interpretations to define

spatial extents of food deserts and swamps using the

RFEI, Expanded RFEI, Modified RFEI (mRFEI)

developed by the Centers for Disease Control (2011)

and Food Balance Metric (Gallagher, 2006) as

guidelines.

In studies that model the supply and demand

forces from farm to plate at a national scale, it is

necessary to have geospatial data regarding farm

locations, their arrangement, land cover, flood plains,

rivers, climate and population change which support

burgeoning sustainable planning, management and

development efforts, especially in developing

countries (Soneye, 2014; Babtunde, Omotesh and

Sholatan, 2010; Obioha, 2009). At this most basic

level, food security at the national scale can be

thought of as a function of the socio-economic and

political environment regarding factors such as

macro-economy, natural resource endowment,

market conditions, education, policy environment,

food safety/quality and health care practices. These

are not considerations in local-scale analysis where

distances, drive times or derivations of these

measures with respect to known food sources are

calculated alongside explanatory variables to

delineate food-needy regions.

A Public Participatory Approach toward the Development of a Comprehensive Geospatial Database in Support of High-scale Food Security

Analysis

While food security does exist at a variety of

scales, the geospatial data required for local

(community) level food is scale dependent and

different in nature than data required at a coarser

national or sub-national scale. These geospatial data

required for this type of local research vary in scope,

ranging from roads and businesses to zoning and

municipal boundaries. For example, Van Hoesen et

al. (2013) looked at the quality of food in conjunction

with point-to-point distances along a vector road

network in Vermont that is grouped within polygonal

enumeration units such as towns/townships. Pioneers

in the application of GIS to assess food accessibility

such as Blanchard and Morton (2007), Gallagher

(2006) and McEntee and Agyeman (2010) also used

vector GIS data at some level (individual point,

census block group, tract, etc.) to express food

security. In national-scale analysis of this type,

analyzing thousands to hundreds of thousand sources

traveling to thousands of destinations is resource-

intensive and requires large, ancillary data layers such

as roads in support of this analysis as well as the

abovementioned interpretation to be useful.

In the United States, guidance on the quantitative

assessment of the food environment begins with the

United States Department of Agriculture Food

Access Atlas (https://www.ers.usda.gov/data-

products/food-access-research-atlas/go-to-the-atlas/).

Food access take into account both the availability or

proximity of food sources to residents as well as

having readily-available transportation. Information

collected and mapped at the census tract level

includes the aforementioned food desert metric (low

income and limited access) as shown in Figure 3 as

well as individual components that make up this

metric and ancillary measures such as income,

vehicle access and high-density housing. As shown

in Figure 3, census tracts can take on varying sizes

Figure 3: USDA Food Access Atlas of Southeastern North

Carolina showing low income and low access census tracts.

and shapes. These larger census tracts, one of which

is 322 sq. miles in size, in the middle of the diagram

located in Columbus, Pender and Sampson Counties

in North Carolina are especially problematic because

they may be too large to emphasize high-scale food

security patterns necessary for community-based

research. As a result, high-scale food environment

analysis performed at the block group (D’Acosta,

2015; Wang, 2012; Jiao, 2012) or even pixel

(Mulrooney et al., 2017) scale, which is finer than

census tracts, better articulates local-level patterns

and serves as a focus of this research.

Depending upon the focus and scale of analysis,

the number of points used in the GIS analysis of the

food environment, whether as sources or destinations,

can range from the dozens (Opher, 2010; Love et al.,

2013) to hundreds (Sharkey et al., 2009; McEntee &

Agyeman, 2010) and even thousands (van Hoesen et

al., 2013). As a basis for this research on high-scale

food security in North Carolina, GIS work

(Mulrooney et al., 2017; Major et al., 2018; Love et

al., 2013) highlighted metrics to measure food

security at some level at the block group level. A

variety of disparate themes were used in these studies,

ranging from roads, business locations and rivers to

municipal boundaries, farmers’ markets and

convenience stores. Each of these layers were

developed or extracted at a scale appropriate for

local-scale analysis in order to facilitate decision and

policy making.

While there is boundless value in performing

local-scale food environment analysis using GIS,

little research has been performed on the actual

themes or topics that would be necessary for high-

quality research at a high scale. While many end-uses

only want the end-products of GIS analysis, the

largest cost of any GIS project is developing the data

which go into high-quality research. It goes without

saying that in an era with limited resources such as

personnel, space and time, database developers must

be pointed and direct in the how, when and to what

extent (temporal, spatial and topical) data must be

developed. Attempts have been made to estimate the

actual and tangible costs (Johnson et al., 2017;

Janssen et al., 2012) and value (Bernknopf and

Shapiro, 2015; Garcia-Rojas, 2015) of geospatial

data; however, it is difficult if not impossible to place

a monetary value on the data although various entities

(Koutnik, 1996; Ledbetter, 1996) have tried to

estimate it from a cost-savings approach in the early

days of GIS in the 1990s. Nonetheless, in this day and

age when GIS is omnipresent in all levels of local and

state government, GIS does facilitate informed

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

decision-making, which can be realized a number of

different ways.

In particular, little work has been performed to

determine how important roads are in food security

research at the local level. What about elevation? In

addition to the actual features, there are various

questions about the individual attributes required for

high-quality food desert research. Is income (at the

census tract level) a necessary attribute for sub-

county food desert research? What about road

length? This research explores how can these themes

and attributes can be prioritized when time and

personnel constraints, which are a reality in the

professional world, exist.

Nonetheless, the resources dedicated to data

creation, especially high-quality data, are

extraordinarily high. Early pioneers of GIS

recognized the importance of data quality, not only

from a cost efficiency standpoint, but because of the

legal ramifications in publishing incorrect spatial

information which may lead to accidents or the

misuse of data (Epstein, 1988). Even then, they

understood the compromise between accuracy, the

cost of creating accurate data and the inevitability that

some error will still exist. This compromise is what

Bédard (1987) called uncertainty absorption. Given

that hundreds to thousands of individual features are

required for this type of GIS analysis, it is impossible

to field verify every single feature used in analysis.

Studies (Sharkey and Horel, 2009; Lake, 2015) have

highlighted the inaccuracy of existing geospatial

databases used in the study of food security using

varied field techniques.

Various forms of accuracy exist, to include

horizontal accuracy (distance between actual feature

and GIS representation of feature), attribute accuracy

(description of features matches the field) and

attribute completeness (all attributes have viable

values). The Federal Geographic Data Committee

(FGDC) and spatial data transfer standards (SDTS)

also consider vertical accuracy (error in measured vs.

represented elevation), data lineage (source materials

of data) and logical consistency (compliance of

qualitative relationships inherent in the data structure)

as part of data quality (FGDC, 1997; USGS, 2000).

In some GIS circles, temporal accuracy (age of the

data compared to usage date) and semantic accuracy

or “the quality with which geographical objects are

described in accordance with the selected model” are

also considered elements of data quality (Salge, 1995)

as well as metadata, the formal cataloguing of GIS

data. In addition to better understanding to what

extent different data layers are required for research,

this study will also address these facets of data

quality.

3 PROCEDURES

In order to prioritize data layers, attributes and facets

of data quality, a survey was developed and

distributed to the GIS community that focuses on

local-scale food security research. It is composed of

twelve questions that not only ask about users’ GIS

experience, but also asks users Likert-type questions

about their preferences for particular GIS data layers

(Figure 4) and the attributes attached to those layers

(Figure 5).

As shown in these figures, respondents were

asked to scale responses to these questions on a 5-

point Likert-type scale, representing “Not Applicable

at All” through “Essential to Research”. The Likert

scale uses ordered responses on a bipolar

measurement scale to assess the level of agreement or

disagreement with a statement. Some scales do have

an even number of responses (4, for example), which

force respondents to choose one side of the mean or

the other.

Figure 4: Likert-type assessment used to rate importance of

GIS data themes for use in food desert research. 23 layers

were used in this assessment.

Table 1: Respondents were asked the question “You are

developing a GIS database in order to conduct local-scale

food security analysis. How important are the following

GIS data layers to your research and analysis?” regarding

GIS data layers (street network, for example). The

following scale assigned point values to their answers.

Response Point Value

Not Applicable at All 1

Slightly Important 2

Moderately Important 3

Very Important 4

Essential to Research 5

A Public Participatory Approach toward the Development of a Comprehensive Geospatial Database in Support of High-scale Food Security

Analysis

Figure 5: Likert-type assessment used to rate importance of

attributes for use in local-level food desert research. 18

attributes were used in this assessment.

As applied to rating the various dimensions of

data quality, respondents were given a survey rating

six facets of data quality. An example of this survey

and explanations of these facets are highlighted below

in Figure 6.

Figure 6: Dimensions of spatial data quality that

respondents were asked to rate using online assessment

tool.

This survey was created and distributed to the

food desert community via message boards, e-mails

and online forums in the Fall of 2017 and Spring

2018. 32 respondents answered the survey.

4 RESULTS

4.1 Prioritization of Data Layers

Respondents were asked to rate data layers on 5-point

Likert-type scale ranging from “Not Applicable at

All” to “Essential to Research” where each response

as assigned a point value as highlighted in Table 1.

For each layer, an average based on responses was

computed from the values and Table 1 and ranked

according to all 23 data layers in the survey. For

example, for the Counties data layer, there were two

responses for “Not Applicable at All”, two for

“Slightly Important”, six for “Moderately Important”,

twelve for “Very Important” and the remaining ten

responded with “Essential to Research”. This would

compute to a value of 3.94 and this value would be

ranked among the other 22 data layers selected for

this survey. In this case, the Counties layer ranked 8

amongst the 23 data layers in the questionnaire.

Unsurprisingly the “Grocery Stores” data layer

ranked with the highest with a score of 4.25, followed

closely by “Roads”, “Farmers’ Markets” and “Urban

Areas”. These are highlighted in Table 2.

Table 2: Rank of Layers/Themes as Voted by GIS User

Community.

Rank Layer

1 Grocery Stores

2 Roads

3 Farmers Markets

4 Urban Areas

5 Census Units (block groups, tract, etc.)

6 Cities and Towns

7 Fast-Food Restaurants

8 Counties

9 Bus Routes

10 Businesses (All)

Non-census sub-county units (boroughs,

townships, etc.)

12 Schools

13 Zoning

14 Sidewalks

15 Land Cover

16 States

17 Churches

18 Walking / Jogging Trails

19 Building Footprints

20 Crime

21 Utilities (Electrical / Gas / Cable / Phone)

22 Elevation

23 Golf Courses

In addition, users were asked to name themes not

mentioned in the above list. Themes that were

mentioned include: Community Gardens, Parks,

Greenhouses, Arable Land, Irrigation Pathways,

Rivers, Access to Water, Food Banks, Food

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

Assistance Organizations, Non-Profit Businesses,

Health Agencies, Corner Stores, Partial Markets

(Walgreens, for example), Liquor Stores, Bus Stops

and County Agencies.

4.2 Prioritization of Attributes

The same conventions and number scales were

applied to attributes that may be used to describe data

layers from Table 1. After averaging values marked

by uses, the “Distance to Resource” attribute was

ranked highest, followed by “Income” and “Race

/Ethnicity (by enumeration unit)”. These results are

highlighted in Table 3.

Table 3: Rank of Attributes to Layers/Themes as Voted by

GIS User Community.

Rank Attribute

1 Distance to Nearest Resource

2 Income

Race / Ethnicity (by

enumeration unit)

4 Population Density

5 Average Household Size

6 Population

7 Education Attainment

Housing Status (Owner-

Occupied / Rental / Vacant)

Transportation (# of vehicles

by enumeration unit)

10 Median Age

11 Median Rent Paid

Spending Patterns (by

enumeration unit)

13 Zoning Type

North American Industry

Classification Standard

(NAICS) Code

15 Road Length

16 Building Size

Number of Employees by

Business

18 Speed Limit

4.3 Dimensions of Data Quality

Users were asked to rate six different dimensions of

data quality from 1 (most important) to 6 (least

important). These data dimensions speak to how the

data are created, described and catalogued as part of

the data development process. Scores for each facet

were averaged and ranked. These rankings are

highlighted in Table 4.

Table 4: Rank of Dimensions of Data Quality.

Rank Facet of Data Quality

Positional Accuracy (features such as

stores are located where GIS database

dictates)

Attribute Accuracy (attributes of features

such as feature length or NAICS codes are

correct)

Temporal Accuracy (data currentness is

consistent with study period)

Logical Consistency (how well the logical

relationships between items in the dataset

are maintained)

Semantic Accuracy (data naming

conventions are consistent among data

sources)

6 Cataloging of data lifeline (via Metadata)

5 STANDARDS-BASED

APPROACH TO DATABASE

DEVELOPMENT

Data standards such as the Spatial Data Standards for

Facilities Infrastructure and Environment (SDSFIE)

are used by the Department of Defense (DoD) to

maximize interoperability across installations and

branches by dictating naming conventions, attributes

and domain values for spatial data layers. The name

road_centerline is denoted as “the center of the

roadway as measured from the edge of the paved

surface” and is consistent across all DoD installations

instead of using layer names such as street, streets or

roads. The road_centerline feature class contains 55

attributes. The FGDC has defined data standards for

landmarks, addressing, thoroughfares and parcels

(FGDC, 2011) in order to standardize attributes so

features can geocoded, described and represented

fully and completely. While the development of a

database dedicated solely to food security is still

being realized, point and polygonal features

representing municipal and census-based units such

as zip codes, towns, census tracts and census block

groups should have attributes which rank highly in

this study such as distance to the nearest resource and

access to transportation, as well as socio-economic

indicators such as income, race/ethnicity, education

attainment, population and population density. The

calculation of these attributes may require further

processing or the import of data from various spatial

A Public Participatory Approach toward the Development of a Comprehensive Geospatial Database in Support of High-scale Food Security

Analysis

databases such as the 2010 Census, Esri Demographic

Database, Esri Spending Patterns and American

Community Survey.

In order to catalog both data for this specific

purpose and the processing performed to develop the

database, it is necessary to describe administrative,

structural and descriptive information about the

geospatial data. Metadata serves as an organized

means to describe a dataset, and provides the formal

framework for providing information about a

dataset’s lineage, age and creators using both

qualitative and quantitative entries. In the GIS

community, the FGDC-endorsed Content Standard

for Digital Geospatial Metadata (CSDGM) is slowly

giving way to an International Standards

Organization (ISO)-based metadata standard that

accounts for evolving technologies such as remotely

sensed imagery, online services and ontologies that

did not exist when the CSDGM (formally known as

FGDC-STD-001-1998) was first published.

While more than 400 individual elements

comprise a complete metadata record, the state of

North Carolina has developed a State and Local

Government Profile, based on the ISO 19115, 19115-

1 and 19119 standards that streamlines these 400

elements into about 75 elements that best capture the

necessary information about a data layer which

enable content consistency and improves the search

and discoverability of data through online data

repositories such as NCOneMap. This standard, as

well as guidance for its use, is provided by the North

Carolina Geographic Information Coordinating

Council (NCGICC) through the NCOneMap online

portal (North Carolina Geographic Information

Coordinating Council, 2019).

Using the State and Local Government Profile as

a guideline, data layers developed in support of high-

scale food security research should be cognizant of

the following entries that already exist within this

profile which speak explicitly to data quality and data

discoverability:

1) Process Description: A repeatable element that

provides a description of how the data were

created and indicate the data source, where

applicable. This process description should

include any geoprocessing and/or field

calculations used to derive spatial and attribute

data derived for the sole purpose of food security

research. This process description should also

contain the source scale denominator and

publication date of source information, where

available to clarify positional and temporal

accuracy respectively.

2) Topic Category: A theme keyword that adheres

to at least one of the ISO Topic Categories.

3) Feature Catalogue: Entity and Attribute

Descriptions and Citations referenced to ISO

19110, where possible.

In addition, the following Data Quality elements not

explicitly addressed in this profile should be

completed to catalog attempts to maintain the highest

possible accuracies given this scale of analysis.

While not required, this cataloguing should strive to

achieve popular positional (horizontal and vertical)

accuracy standards such as the National Mapping

Accuracy Standards (NMAS) for paper maps (United

States Bureau of the Budget, 1947) and more recent

National Standard for Spatial Data Accuracy

(NSSDA) used for digital data (Federal Geographic

Data Committee, 1998)

1) Attribute Accuracy Report: an explanation of the

accuracy of the identification of the entities and

assignments of values in the data set and a

description of the tests used. This may be useful

if food sources and/or destinations have been

field checked for attribute errors.

2) Quantitative Attribute Accuracy Assessment: a

value assigned to summarize the accuracy of the

identification of the entities and assignments of

values in the data set and the identification of the

test that yielded the value.

3) Attribute Accuracy Value: an estimate of the

accuracy of the identification of the entities and

assignments of attribute values in the data set.

4) Logical Consistency Report: an explanation of

the fidelity of relationships in the data set and

tests used. This may be applicable if data used in

the same analysis or derivation of attributes come

from multiple data sources and/or at different

scales.

5) Completeness Report: information about

omissions, selection criteria, generalization,

definitions used, and other rules used to derive

the data set. Useful for both spatial data and

attribute completion.

6) Horizontal Positional Accuracy Report: an

explanation of the accuracy of the horizontal

coordinate measurements and a description of the

tests used. This may be useful when field

checking the locations of food sources and/or

destinations.

7) Horizontal Positional Accuracy Value: an

estimate of accuracy of the horizontal positions

of the spatial objects.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

8) Horizontal Positional Accuracy Explanation:

the identification of the test that yielded the

Horizontal Positional Accuracy Value.

9) Vertical Positional Accuracy Report (where

applicable): an explanation of the accuracy of

the vertical coordinate measurements and a

description of the tests used (FGDC 2000).

6 CONCLUSIONS

Food security entails the ability, whether it be at the

individual, community or national scale, to procure

nutritious and affordable food. While GIS has

increasingly become a powerful tool to map spatial

dimensions of food security and the factors that help

explain it, little research has been performed to

determine what themes are useful in local-level food

security research. Given that data and the people that

develop it are the most expensive component of any

GIS project, this is especially important when limited

resources exist. This data development can take on

many forms, ranging from the downloading of

existing data, extraction from currently existing

databases, the creation of brand-new spatial data via

digitization, geocoding or the use of remotely sensed

imagery, either purchased, procured or captured using

a UAS (Unmanned Aircraft System). Regardless of

the method, time and personnel resources must be

utilized in order to derive the attributes that facilitate

food security research while cataloguing these

people, processes and resources.

The database requirements for food security

analysis in the digital environmental at a local scale

are much different than those needs at the

national/sub-national scale. National scale and sub-

national (state) studies in food security explore the

economics of food production and links between this

food and those who need it using data such as land

cover, soil type, low-scale transportation networks

(both road and railroad), state and county outlines

using coarse and general data. High-scale analysis at

the block group and even pixel scale requires more

specialized data, analysis, attribution and cataloguing

than data grouped at tracts, the standard for a lot of

research, including the United States Department of

Agriculture Food Access Atlas, as well as more

coarse zip codes and counties. From a data

development standpoint, the realization of a database

in support of local-scale food security research

requires a reconciliation between developing the

correct data layers while developing them at an

appropriate scale that allows for local-level (sub

county) scale analysis.

In a survey of 32 GIS professionals who utilize

GIS data in support of food security research,

questions were asked about their opinions of various

themes and their relative importance in food security

research. Themes directly related to the food

environment and food accessibility such as grocery

stores, roads and farmers’ markets were ranked

highest by these GIS professionals. In addition, sub-

county census units such as census tracts and block

groups were ranked higher than counties, highlighting

the need for higher-scale data compared to the coarser

county-level data.

In addition, attributes used to describe these

themes were prioritized in this survey. Information

related to distance (more specifically distance from

resources) and socio-demographic indicators such as

income, race/ethnicity and household size ranked

amongst the highest in the GIS community. This ties

in directly with food desert research and specifically

the USDA definition of a food desert, which utilize

both distance and poverty components. Lastly,

various dimensions of data quality exist and users

were asked to rank them in their order of importance.

Positional accuracy and attribute accuracy ranked the

highest while the cataloguing of data in the form of

data was ranked the lowest.

The specific focus of this work has been on the

collection, integration, analysis, assessment and

description of geospatial data that is of a type and

level of detail to be of practical value in the

development, implementation and evaluation of

interventions addressing food security. While the

results of this work can be used as pure research in

and of itself, it is anticipated that results can be used

in helping to facilitate decision-making and formulate

policy at directly addressing and remediating the

phenomenon of food deserts. Furthermore, it

addresses the technical aspects of geospatial database

development such as attribution, naming conventions

and metadata according to existing standards such as

the ISO-based North Carolina State and Local

Government Metadata Profile. While some minor

questions still remain unanswered such as the

potential for cross-validation or the use of qualitative

data given that food desert research has been trending

towards a mixed-methods approach combining

qualitative and quantitative data, it is our hope to

further explore cost-effective methods for needs

assessment that take into account both causal

complexity and programmatic challenges imposed by

the combination of limited resources and increased

demand. Integrating GIS technologies with

intervention planning has the potential to be a cost-

effective means for organizations to conduct effective

A Public Participatory Approach toward the Development of a Comprehensive Geospatial Database in Support of High-scale Food Security

Analysis

planning aimed at improving food and nutritional

security at multiple spaital and temporal scales.

Prudent database development serves as the

cornerstone of this effective planing and

implementation.

ACKNOWLEDGEMENTS

The project was supported by the Agricultural and

Food Research Initiative Competitive Program of the

USDA National Institute of Food and Agriculture

(NIFA), grant number 2016-67023-24904.

This material is also based upon work supported by

the National Science Foundation under Grant No.

1824949. Any opinions, findings, and conclusions or

recommendations expressed in this material are those

of the author(s) and do not necessarily reflect the

views of the National Science Foundation.

REFERENCES

Apparicio P., Cloutier M.S. and Shearmur, R. 2007. The

case of Montreal’s missing food deserts: evaluation of

accessibility to food supermarkets. International

Journal of Health Geographics 6:4: 1-13.

Babatunde R.O., Omotesho O.A. and Sholatan, O.S. 2010.

Factors influencing food security status of rural farmers

in North Central Nigeria. Agricultural Journal 2(3):

351-357.

Bedard, Y. 1987. Uncertainties in land information systems

databases. Proceedings of the 8th International

Symposium on Computer Assisted Cartography (Auto

Carto 8), (pp. 175-184). Baltimore, MD.

Bernknopf, R. and Shapiro, C. 2015. Economic assessment

of the use value of geospatial information. ISPRS

International Journal of Geo-Information 4: 1142–

1165. doi: 10.3390/ijgi4031142

Blanchard, T. & Lyson, T. 2006. Access to low cost

groceries in nonmetropolitan counties: Large retailers

and the creation of food deserts. Paper presented at the

Measuring Rural Diversity Conference, Washington,

DC [online]. Retrieved from: http://srdc.msstate.edu/

trainings/presentations_archive/2002/2002_blanchard.

pdf

Block J.P., Scribner R.A. and DeSalvo K.B. 2004. Fast

food, race/ethnicity, and income: a geographic analysis.

American Journal of Preventative Medicine 27: 211–

217.

Both, A., Wauer, M., Garcia-Rojas, A, Hladky, D and

Lehmann, J. 2015. GeoKnow Generator Workbench –

An integrated tool supporting the linked data lifecycle

for enterprise usage. Proceedings of the 11th

International Conference on Semantic Systems Posters

and Demos, of SEM '15. 92-92.

Burns, C. and Inglis, A. 2007. Measuring food access in

Melbourne: access to healthy and fast foods by car, bus

and foot in an urban municipality in Melbourne. Health

Place 13: 877–885.

California Center for Public Health Advocacy. 2007.

Searching for Healthy Food: The Food Landscape in

California Cities and Counties [online]. Retrieved

from http://www.publichealthadvocacy.org/RFEI/

expanded percent20methods.pdf

Centers for Disease Control and Prevention. 2011.

Children’s Food Environment State Indicator Report,

2011 [online]. Retrieved from http://www.cdc.gov/

obesity/downloads/ChildrensFoodEnvironment.pdf

Chen, X. and Clark, J. 2013. Interactive three-dimensional

geovisualization of space–time access to food. Applied

Geography 43: 81–86.

Chilton, M. and Rose, D. 2009. A Rights-Based Approach

to Food Insecurity in the United States American

Journal of Public Health 99: 1203_1211, https://doi.

org/10.2105/AJPH.2007.130229

Clarke, G., Heather E., and Guy, C. 2002. Deriving

Indicators of Access to Food Retail Provision in British

Cities: Studies of Cardiff, Leeds and Bradford. Urban

Studies 39(11): 2041-2060.

Clary C.M., Ramos Y., Shareck, M., and Kestens, Y. 2015.

Should we use absolute or relative measures when

assessing foodscape exposure in relation to fruit and

vegetable intake? Evidence from a wide-scale Canadian

study. Preventative Medicine (Baltimore) 71: 83–87.

Cooksey-Stowers, K, Schwartz, M. and Brownell, K. 2017.

Food Swamps Predict Obesity Rates Better than Food

Deserts in the United States. International Journal of

Environmental Research and Public Health 14: 1366.

D’Acosta, J. 2015. Finding food deserts: a study of food

access measures in the phoenix-mesa urban area

(Master’s Thesis). University of Southern California.

Los Angeles, CA.

Donkin, A. J., Dowler, E. A., Stevenson, S. J., & Turner, S.

A. 1999. Mapping access to food at a local level. British

Food Journal 101(7): 554.

Epstein, E. F. 1988. Litigation over information: The use

and misuse of maps. Proceedings, IGIS: The Research

Agenda 1 (pp. 177-184). Washington, D.C.: NASA.

Food and Agriculture Organization of the United Nations.

2009. Declaration of the World Summit on Food

Security [online]. Retrieved from

ftp://ftp.fao.org/docrep/fao/Meeting/018/k6050e.pdf

Federal Geographic Data Committee (FGDC). 1998.

Geospatial Positioning Accuracy Standards Part 3:

National Standard for Spatial Data Accuracy. FGDC-

STD-007.3-1998.

Federal Geographic Data Committee (FGDC). 2000.

Content Standard for Digital Geosp atial Metadata

Workbook. Washington D.C.: Federal Geographic

Data Committee.

Federal Geographic Data Committee (FGDC). 2011.

United States Thoroughfare, Landmark and Postal

Address Data Standard. Washington D.C.: Federal

Geographic Data Committee.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

Furey, S., Strugnell, C. and McIlveen, H. 2001. An

Investigation of the Potential Existence of “Food

Deserts” in Rural and Urban Areas of Northern Ireland.

Agriculture and Human Values 18: 447–457.

Gallagher M. 2006. Examining the impact of food deserts

on public health in Chicago. Mari Gallagher Research

and Consulting Group [online]. Retrieved from

http://www.marigallagher.com/site_media/dynamic/pr

oject_files/1_ChicagoFoodDesertReport-Full_.pdf

Gross, J. and Rosenberger, N. 2005. Food Insecurity in

Rural Benton County: An Ethnographic Study.

Working Paper No. 05-02, Rural Studies Program,

Oregon State University, Corvallis, OR.

Guy, C. M., Clarke, G., & Eyre, H. 2004. Food retail change

and the growth of food deserts: a case study of Cardiff.

International Journal of Retail & Distribution

Management, 32: 72–88.

Hallett, L. and McDermott, D., 2011. Quantifying the

extent and cost of food deserts in Lawrence, Kansas,

USA. Applied Geography 31: 1210–1215.

Jago R., Baranowski T., Baranowski J.C. 2007. Distance to

food stores and adolescent male fruit and vegetable

consumption: mediation effects. International Journal

of Behavioral, Nutritional and Physical Activity 4:35.

Janssen, M., Charalabidis, Y. and A. Zuiderwijk. 2012.

Benefits, adoption barriers and myths of open data and

open government. Information Systems Management

29(4): 258 – 268.

Jiao, J., Moudon, A., Ulmer, J., Hurvitz, P. and

Drewnowski, J. 2012. How to identify food deserts:

Measuring physical and economic access to

supermarkets in King County, Washington. American

Journal of Public Health 102(10): 32–39.

Johnson, P. A., Sieber, R., Scassa, T., Stephens, M. and

Robinson, P. 2017. The Cost (s) of Geospatial Open

Data. Transactions in GIS 21: 434–445,

Koutnik, Mike. 1996. The Benefits of GIS in Local

Government: Examples from ESRI’s User Community.

Environmental Systems Research Institute, Inc.

(Unpublished).

Ledbetter, M., Carder Hunt and Don Anderson. 1997.

Blueprints for a Citywide GIS: Scottsdale’s Award-

Winning System Provides a Profitable Example. GIS

World, November, 62-64.

Lewis, L.B., D.C.Sloane, L.M. Nascimento, A.L. Diamant,

J.J. Guinyard, A.K. Yancey, G. Flynn. 2005. "African

Americans' Access to Healthy Food Options in South

Los Angeles Restaurants." American Journal of Public

Health 95(4): 668-73.

Love, G., Mulrooney, T., and Brown, L. 2012. Using GIS

to address food availability in Durham, North Carolina.

The North Carolina Geographer 19: 34-52.

Lovett, A., Haynes, R., Sunnenberg, G., & Gale, S. 2002.

Car travel time and accessibility by bus to general

practitioner services: a study using patient registers and

GIS. Social Science & Medicine. 55(1): 97–111.

Major E., Delmelle E., Delmelle E. 2018 SNAPScapes:

Using geodemographic segmentation to classify the

food access landscape. Urban Science 2:71. doi:

10.3390/urbansci2030071.

Mamen, Katy. 2007. “Facing Goliath: Challenging the

Impacts of Supermarket Consolidation on our Local

Economies, Communities, and Food Security.” Policy

Brief, The Oakland Institute 1(3): 1-8.

McEntee, J., and Agyeman, J. 2010. Towards the

development of a GIS method for identifying rural food

deserts: Geographic access in Vermont, USA. Applied

Geography 30(1): 165–176.

Morton, L., Bitto, E. and Oakland, M. 2005. Solving the

Problems of Iowa Food Deserts: Food Insecurity and

Civic Structure. Rural Sociology 70(1): 94–112.

Morton, L., and Blanchard, T. C. 2007. Starved for access:

Life in Rural America’s food deserts. Rural Realities

1(4):1–10.

Mulangu, F. and Clark, J. 2012. Identifying and measuring

food deserts in rural Ohio. Journal of Extension, 50(3):

3FEA6. Retrieved from: http://www.joe.org/joe/

2012june/a6.php.

Mulrooney, T., McGinn, C., Branch, B., Madumere, C. and

Ifediora, B. 2017. A New Raster-Based Metric to

Measure Relative Food Availability in Rural Areas: A

Case Study in Southeastern North Carolina.

Southeastern Geographer 57(2): 151 - 178.

North Carolina Geographic Information Coordinating

Council (NCGICC). 2018. North Carolina State and

Local Government Metadata Profile for Geospatial

Data and Services [online]. Retrieved from

http://www.nconemap.gov/DiscoverGetData/Metadata

.aspx#iso.

Obioha, E.E. 2009. Climate Variability, Environment

Change and Food Security Nexus in Nigeria. Journal of

Human Ecology 26(2): 107-121.

Opfer, P. 2010. Using GIS technology to identify and

analyze ‘food deserts’ on the southern Oregon coast.

Oregon State University Archives 1–52.

Pearce, J., Witten, K., & Bartie, P. 2006. Neighbourhoods

and health: a GIS approach to measuring community

resource accessibility. Journal of Epidemiology and

Community Health 60(5): 389–395.

Powell, L. M., Slater, S., Mirtcheva, D. Bao, Y. and

Chaloupka, F. 2007. Food Store Availability and

Neighborhood Characteristics in the United States.

Preventive Medicine 44:198-95.

Rose, D. and Richards, R. 2004. Food Store Access and

Household Fruit and Vegetable Use Among

Participants in the US Food Stamp Program. Public

Health Nutrition 7(8):1081-8.

Rose D., Bodor J.N., Swalm C.M., Rice J.C., Farley T.A.,

Hutchinson P.L. 2009. Deserts in New Orleans?

Illustrations of urban food access and implications for

policy. Paper presented at: University of Michigan

National Poverty Center/USDA Research Conference

on Understanding the Economic Concepts and

Characteristics of Food Access; February 2009;

Washington, DC [online]: Retrieved from:

http://www.npc.umich.edu/news/events/food-access/

rose_et_al.pdf.

Salgé, F. 1995. Semantic accuracy. In S.C. Guptill and J.L.

Morrison (Eds.), Elements of Spatial Data Quality (pp.

139-152). New York: Elsevier Science Ltd.

A Public Participatory Approach toward the Development of a Comprehensive Geospatial Database in Support of High-scale Food Security

Analysis

Sharkey, J. R., and Horel, S., 2008. Neighborhood

socioeconomic deprivation and minority composition

are associated with better potential spatial access to the

ground-truthed food environment in a large rural area.

Journal of Nutrition 30(5): 620–627.

Soneye, Alabi. 2014. Farm Holdings in Northern Nigeria

and Implication for Food Security: A Remote Sensing

and GIS Assessment. African Journal of Food and

Agriculture, Nutrition and Development 14(2): 1-15.

United States Bureau of the Budget, 1947, United States

National Map Accuracy Standards: U.S. Bureau of the

Budget, Washington, D.C.

United States Geological Survey (USGS). 1997. Spatial

Data Transfer Standard (SDTS): Logical

specifications. Reston, Virginia: United States

Geologic Survey.

Van Hoesen, J., Bunkley, B. & Currier, C. 2013. A GIS-

based methodology toward refining the concept of rural

food deserts: a case study from Rutland County,

Vermont. Journal of Agriculture, Food Systems and

Community Development 3(2): 61-76.

Ver Ploeg, M., Breneman, V., Farrigan, T., Hamrick, K.,

Hopkins, D., et al. 2009. Access to affordable and

nutritious food–measuring and understanding food

deserts and their consequences: Report to congress.

Washington, D.C.: United States Department of

Agriculture, Economic Research Service.

Wang, Z. 2012. Socio-demographic and economic

determinants of food deserts (Master’s Thesis).

University of Arkansas. Fayetteville, AR.

Wrigley, N. 2002. ‘Food Deserts’ in British cities: policy

context and research priorities. Urban Studies 39(11):

2029–2040.

Zenk S.N., Powell, L.M., Rimkus, L., Isgor, Z. and Barker,

D.C. 2014. Relative and absolute availability of

healthier food and beverage alternatives across

communities in the United States. American Journal of

Public Health 104: 2170–2179.

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