This study links social network analysis (SNA) with GIS in the examination of First Nation food sharing. Introducing spatial information into conventional SNA offers new perspectives and facilitates a better understanding of network data. Multiple GIS visualizations were used to complement the understanding of the network's multiple dimensions. Spatial statistics were carried out to test key hypotheses about the relationship between spatial proximity and social proximity in the food-sharing network. Results show both distance and kinship are important variables in explaining food sharing patterns.
Within the social sciences literature,
numerous studies examined the role spatial
proximity plays on social tie formation. This
research found that people are more likely
to be friends if they are geographically close
Most studies use spatial proximity as the main factor contributing to tie formation. Verderey et al. (2012) draw attention to the fact that in the case of kinship networks, the closeness of relationships between family members also influences where members of these communities decide to live.
Social Network Analysis (SNA) is widely used to answer questions related to individuals’ patterns of interaction, cohesion, social influence, and proximity. However, it is not spatially explicit. Mapping spatially referenced social interactions and activities allows us to uncover spatial patterns, associations, and ask new questions of network data (Logan, 2012). This motivated the current study. Specifically this study set out to examine the spatial parameters that influence food sharing between members of the Saulteau First Nations (SFN).
The SFN is located in northeast British
Columbia. The population of SFN is 380, living
in 125 on-reserve households
Within the SFN community, sharing harvested wild foods is prominent. Food sharing serves numerous functions, including the alleviation of household food insecurities, continuance of a cultural tradition, and social cohesion. In general, the SFN maintains strong family bonds. Kin continue to depend on each other for their social and economic wellbeing.
What requires more attention is the relationship between social ties and physical proximity. In this research, we measured physical proximity using the Euclidean distance, which was used to explore its relationship with social proximity (i.e. kin/nonkin, nuclear/distant kin) by answering the following: 1. Do kin live closer1 to each other than non-kin? 2. Do nuclear family members live closer than extended kin does? 3. Within kin, is there an association between living close to one another and food sharing? 4. Are households located further from the community’s core less engaged in food sharing? 5. Do households located further from the community’s core receive less food? 6. Are households located closer to the community core2 engaging in more food sharing? The first part of our analysis involves visualizing the network data. This elucidated the spatial distribution of SFN’s local and regional food sharing. In the second part of this paper, we focus on testing multiple hypotheses related to spatial and social proximity. 1 In this study, the term “close(r)”means physical proximity 2 Community “core” defined as the geographic center of the community.
2.1 Data
Data used in this study comes from a larger
research project focused on the Assessment
of First Nations Environmental Livelihoods
in Northeast British Columbia
Household surveys conducted in the SFN were used to assess how environmental change might affect their harvest and subsequent food sharing. The first part of the survey focused on wildlife harvesting and the second part of the questionnaire collected data on food sharing. Responses were compiled into several spreadsheets for analysis. Due to lack of spatial information for 13 households, 154 out of the 179 food exchange records were used in the final analysis.
A modified 10x10 km grid map of the T8TA territory was developed with GIS. This allows us to visualize the concentration of resource harvesting, in terms of household land use, food weight, by species, travel distance, and ecological values including land cover and habitat fragmentation.
Households’ locations came in PDF format from Peace River Regional District (dated September 30th, 2014) showing land surface features as well as the location of 167 recognized households on Moberly Lake. Household locations were georeferenced and extracted. Certain households were not located because (1) the reference layer was outdated; or, (2) survey data collection errors.
Locations of regional communities were determined using Google’s geolocator service followed by manual revisions for quality control. There were in total 77 households involved in the final analysis.
To ensure the integrity of the network dataset and account for unknown or missing the label “unknown location” was used. However, with missing data, there is less certainty over the reliability of findings, which is an important limitation to recognize. In the future development of this research we plan to incorporate various imputation techniques, already established in the SNA domain, to mitigate against missing data related errors. 2.2 Software 1. Python and R were used in this study to process the original data for the initial exploratory analysis and preparation for subsequent analysis. 2. UCINET was used for statistical testing and SNA descriptive statistics. Sociograms were created with NetDraw. 3. ArcGIS was used in this project to manage geodatabases as well as data manipulation, visualization and spatial analysis. 4. Interactive web-based SNA sociograms were programed as custom single page web applications. The backend was powered by Apache and PHP. HTML, JavaScript and CSS were used for the front-end development.
The workflow started with social data collection through a structured survey (Figure 1). Surveys were cleaned and processed before analysis. Analysis was done in three steps. 1. Conventional SNA, which helped to explore the dataset through visualization and performing non-spatial statistics as well as hypotheses testing. 2. Visualization and spatial analysis using desktop GIS and WebGIS that facilitate exploratory analysis, interpretation and prepare distances for investigating of SFN’s food sharing network. 3. Based on the spatial data and findings from the previous two steps, testing hypotheses on the interaction of spatial proximity and social proximity.
A structured survey was conducted. Original paper results were scanned into PDF format from correspondents in SFN; some responses were handwritten. A spreadsheet template was used to manually transcribe each survey, resulting in 150 individual spreadsheets for all households in SFN. Custom Python and R scripts were developed to reformat these spreadsheets into formats suitable for data visualization and analysis.
Several visualizations were used to explore the food sharing network dataset. A sociogram is an effective way of visualizing social network data. It depicts relationships among specific groups, with the aim of discovering underlying relationships (Figure 2). A desktop SNA software and web-based solutions were used to prepare the sociograms3. The latter was specifically designed to streamline the procedure from raw data 3 The term sociogram only partially depicts what these diagrams are. We are considering using an alternative term – sociomap, to better label these figures. to final presentation and dissemination to stakeholders.
Conventional sociograms are not spatially explicit. Desktop GIS and WebGIS were used to process data for various geographic visualizations. However, visualizing social network data presents unique challenges, including multiple scales (e.g. regional and household), multiple dimensions (e.g. different food categories), directions of ties (e.g. giving and receiving in food sharing), multiple weights of nodes and ties, as well as overlapping ties that interfere with each other and impede visualization. A custombuilt online solution was developed.
To determine the role of spatial proximity in SFN’s food sharing network, student-t test was conducted to compare the food sharing (measured by food quantity, mean Euclidean distance) categorized by kinship type. Permutation based t-tests and other SNA descriptive statistics were conducted in UCINET.
To measure spatial autocorrelation we used both Moran’s I and Geary’s C. Moran's I ranges from +1 - strong positive spatial autocorrelation, to -1 - strong negative spatial autocorrelation, with 0 indicating a random pattern. For Geary’s C, a value of 1 denotes no association, less than 1 a negative autocorrelation, whereas a value greater than 1 denotes positive autocorrelation.
Households’ activity levels within the
foodsharing network were measured using
indegree centrality scores in order to assess
how much they received. Out-degree
centrality scores were calculated to assess how
much a household shared with others. The
number of vertices4 adjacent to a given
vertex in a symmetric graph is the degree of
that vertex, also referred to as Freeman
degree centrality. In a directed or
nonsymmetrical graph, we further distinguish
4 Households are the graphs’ vertices, and
foodsharing interactions are ties.
between in-degrees and out-degrees
The main objective of this study is to bridge SNA and GIS to reveal hidden patterns. Such link could be easily implemented through the loose coupling of SNA and GIS via a thematic map. Figure 2 shows how the sociogram was usually rendered inside SNA software. In this case, the food-sharing network inside SFN is visualized at the household level. At the regional level, with all communities’ locations defined, a GIS can display a thematic map, or a geographic sociogram (Figure 3). Moreover, the previous household-scale sociogram is embedded to offer a complete view of the food-sharing network for SFN.
Though sociograms could be made using desktop software, web-based visualizations were also investigated. Moreover, webbased visualizations offer interactive features that cannot be achieved through a loose coupling of desktop SNA and GIS. To understand the pressure of harvesting activities on the landscape, a grid-based web sociogram was created (Figure 6 and 7). This web application offers a graph view and a map view. The graph view displays the food-sharing network for any chosen resource category. It also differentiates households by their selected roles. The map view links the social network to the landscape. As a result, the researcher can trace the food extracted within a 10x10km2 grid and how it was shared within the social network.
3.3.1 Social Proximity vs. Spatial Proximity One of the main objectives of this paper is to understand if there is a statistically significant difference attributable to physical distances between kin and non-kin households, and between nuclear and extended kin. Results show that within the SFN community, family members live closer to one another than non-kin. However, this difference is not statistically significant. When compared to members of the extended family, members of the nuclear family live closer together. This difference was not statistically significant either. We were interested in whether kin that live close to one another share more food. We calculated Geary and Moran’s I statistics to answer the question. Results show a weak negative tendency between degree centrality and distance to kin (G=1.43, p=0.1; MI=0.24, p=0.08), however this autocorrelation holds at p=0.1. In other words, there seems to be a tendency for kin who live close to one another to exchange more, if we accept a pvalue of 0.1. Furthermore, given that this result was obtained using an incomplete kinship dataset, further research is required to consolidate this finding.
When we look at the entire dataset, irrespective of whether households exchanging food are related, households living close to each other tend to share more food (G=2.08, p=0.01). This should be interpreted with caution, since we restricted our foodsharing network to exchanges taking place only within the SFN community. 3.3.2 Social Engagement vs. Spatial Proximity Similar analysis was performed to answer the remaining three questions. In the following section, distances represent the distance of households from the community’s geographic center (‘core’).
Are households located further from the community’s core less engaged in food sharing? Levels of engagement in the food-sharing network were measured using Freeman’s Degree Centrality scores. Households with at least four connections were considered highly engaged in the food-sharing network. Results show that less engaged households live, on average, closer to the community’s geographic core. However, there was no statistical difference (p = 0.49). This result needs to be further refined – spatial clustering in the data means that there are multiple community foci. Do households located further from the community’s core receive less food? In-degree centrality scores quantify the total number of incoming ties for each household. In the SFN food-sharing network, this score represents the number of times a household received food. Based on these scores, we created a dummy variable for high receivers and low receivers (receiving food above or below three times). Results show that households receiving less food are located, on average, closer to the community’s core when compared to high receivers. This difference was not statistically significant (p=0.79). Do households located in the core of the community give more food? Households located closer to the core of the community are not giving a lot of food to other community members. On the contrary, households giving more food were located slightly further from the core, when compared to non-high givers (out-degree centrality < 3). However, the difference in mean distance from the core between these two groups was not statistically significant (p = 0.97).
High Givers
Non-High
Givers By incorporating spatial information into social network analysis, this study investigated visualization options and discussed the contribution of spatial variables within the social network.
Spatially explicit statistics were used to test key hypotheses about the role of spatial proximity and social proximity in the local food-sharing network. Both geographic distances and kinship are important variables in explaining food-sharing patterns. Further research is essential, especially in the context of kinship networks where social proximity can reinforce spatial proximity and vice versa. Moreover, impacts of different measures of spatial distances need to be evaluated.
Furthermore, this line of inquiry needs further advancement, by building on anthropological and social studies realized within the context of Canadian indigenous communities.
The British Columbia Ministry of Forests, Lands and Natural Resource Operations provided funding for this research. We are grateful to them for their support as well as for the support and collaboration of the SFN. The authors are thankful to The Spatial Initiative and the Social Network Lab for their commitment in this project.