Spatial Knowledge and Information Canada, 2019, 7(5), 4 Examining the Relationship Between Spatial and Social Proximity in First Nation Food Sharing ANA-MARIA BODGAN1, MENG LI2, DAVID NATCHER3, ABIGAEL RICE3, RONG SHEN2, WEIPING ZENG2, JASON DISANO1, SCOTT BELL4 1Social Sciences Research Laboratories, [email protected], 2The Spatial Initiative, [email protected], 3Agricultural and Resource Economics, [email protected], 4Geography and Planning, [email protected], University of Saskatchewan, Canada ishes as spatial distances increase (Hare, ABSTRACT 1973; Latané et al., 1995). This study links social network analysis Verdery et al. (2012) examined the relation- (SNA) with GIS in the examination of First ship between kinship and spatial proximity. Nation food sharing. Introducing spatial in- They used spatially referenced kinship net- formation into conventional SNA offers new works and found a positive correlation be- perspectives and facilitates a better under- tween closeness of kin and households spa- standing of network data. Multiple GIS vis- tial proximity, attributed to close kin co- ualizations were used to complement the habitation. understanding of the network's multiple dimensions. Spatial statistics were carried Most studies use spatial proximity as the out to test key hypotheses about the rela- main factor contributing to tie formation. tionship between spatial proximity and so- Verderey et al. (2012) draw attention to the cial proximity in the food-sharing network. fact that in the case of kinship networks, the Results show both distance and kinship are closeness of relationships between family important variables in explaining food shar- members also influences where members of ing patterns. these communities decide to live. Social Network Analysis (SNA) is widely 1. Introduction used to answer questions related to individ- uals’ patterns of interaction, cohesion, social Within the social sciences literature, nu- influence, and proximity. However, it is not merous studies examined the role spatial spatially explicit. Mapping spatially refer- proximity plays on social tie formation. This enced social interactions and activities al- research found that people are more likely lows us to uncover spatial patterns, associa- to be friends if they are geographically close tions, and ask new questions of network da- (Festinger et al., 1950; Feld and Carter, 1998; ta (Logan, 2012). This motivated the current Mouw and Entwisle, 2006), whereas those study. Specifically this study set out to ex- who live further from a community “core” amine the spatial parameters that influence tend to be socially isolated (Festinger et al., food sharing between members of the 1950). The physical location of one’s place of Saulteau First Nations (SFN). residence further increases the likelihood of strong social tie formation with others in The SFN is located in northeast British Co- close proximity (Coombs 1973) and dimin- lumbia. The population of SFN is 380, living in 125 on-reserve households (Statistics 2 Examining the Relationship Between Spatial and Social Proximity in First Nation Food Sharing Canada, 2016). SFN economy is a mixed 2. Methods and Data economy, which includes wage earning and wildlife harvesting activities. 2.1 Data Within the SFN community, sharing har- Data used in this study comes from a larger vested wild foods is prominent. Food shar- research project focused on the Assessment ing serves numerous functions, including of First Nations Environmental Livelihoods the alleviation of household food insecuri- in Northeast British Columbia (Lu et. al., ties, continuance of a cultural tradition, and 2019). social cohesion. In general, the SFN main- Household surveys conducted in the SFN tains strong family bonds. Kin continue to were used to assess how environmental depend on each other for their social and change might affect their harvest and sub- economic wellbeing. sequent food sharing. The first part of the What requires more attention is the rela- survey focused on wildlife harvesting and tionship between social ties and physical the second part of the questionnaire collect- proximity. In this research, we measured ed data on food sharing. Responses were physical proximity using the Euclidean dis- compiled into several spreadsheets for anal- tance, which was used to explore its rela- ysis. Due to lack of spatial information for tionship with social proximity (i.e. kin/non- 13 households, 154 out of the 179 food ex- kin, nuclear/distant kin) by answering the change records were used in the final analy- following: sis. 1. Do kin live closer1 to each other than A modified 10x10 km grid map of the T8TA non-kin? territory was developed with GIS. This al- 2. Do nuclear family members live closer lows us to visualize the concentration of re- than extended kin does? source harvesting, in terms of household 3. Within kin, is there an association be- land use, food weight, by species, travel dis- tween living close to one another and tance, and ecological values including land food sharing? cover and habitat fragmentation. 4. Are households located further from the Households’ locations came in PDF format community’s core less engaged in food from Peace River Regional District (dated sharing? September 30th, 2014) showing land surface 5. Do households located further from the features as well as the location of 167 recog- community’s core receive less food? nized households on Moberly Lake. House- 6. Are households located closer to the hold locations were georeferenced and ex- 2 community core engaging in more food tracted. Certain households were not located sharing? because (1) the reference layer was outdated; The first part of our analysis involves visual- or, (2) survey data collection errors. izing the network data. This elucidated the Locations of regional communities were de- spatial distribution of SFN’s local and re- termined using Google’s geolocator service gional food sharing. In the second part of followed by manual revisions for quality this paper, we focus on testing multiple hy- control. There were in total 77 households potheses related to spatial and social prox- involved in the final analysis. imity. To ensure the integrity of the network da- taset and account for unknown or missing the label “unknown location” was used. 1 In this study, the term “close(r)”means physical However, with missing data, there is less proximity certainty over the reliability of findings, which is an important limitation to recog- 2 Community “core” defined as the geographic center nize. In the future development of this re- of the community. search we plan to incorporate various impu- Examining the Relationship Between Spatial and Social Proximity in First Nation Food Sharing 3 tation 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 ex- ploratory analysis and preparation for sub- sequent 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 man- age geodatabases as well as data manipula- tion, visualization and spatial analysis. 4. Interactive web-based SNA sociograms Figure 1. Overall methodology of this paper were programed as custom single page web applications. The backend was powered by Apache and PHP. HTML, JavaScript and 2.3.2 Survey data reformatting CSS were used for the front-end develop- A structured survey was conducted. Original ment. paper results were scanned into PDF format 2.3 Methods from correspondents in SFN; some respons- es were handwritten. A spreadsheet tem- 2.3.1 Overall methodology plate was used to manually transcribe each The workflow started with social data collec- survey, resulting in 150 individual spread- tion through a structured survey (Figure 1). sheets for all households in SFN. Custom Surveys were cleaned and processed before Python and R scripts were developed to analysis. Analysis was done in three steps. reformat these spreadsheets into formats suitable for data visualization and analysis. 1. Conventional SNA, which helped to ex- plore the dataset through visualization and 2.3.3 Data visualization performing non-spatial statistics as well as Several visualizations were used to explore hypotheses testing. the food sharing network dataset. A socio- 2. Visualization and spatial analysis using gram is an effective way of visualizing social desktop GIS and WebGIS that facilitate ex- network data. It depicts relationships ploratory analysis, interpretation and pre- among specific groups, with the aim of dis- pare distances for investigating of SFN’s covering underlying relationships (Figure 2). food sharing network. A desktop SNA software and web-based so- 3. Based on the spatial data and findings lutions were used to prepare the socio- from the previous two steps, testing hypoth- grams3. The latter was specifically designed eses on the interaction of spatial proximity to streamline the procedure from raw data and social proximity. 3 The term sociogram only partially depicts what these diagrams are. We are considering using an al- ternative term – sociomap, to better label these fig- ures. 4 Examining the Relationship Between Spatial and Social Proximity in First Nation Food Sharing to final presentation and dissemination to between in-degrees and out-degrees (Free- stakeholders. man, 1979). Conventional sociograms are not