Understanding Spatial Statistics:

Mapping Social Ecological Space




Understanding social ecological systems, or areas where human and ecological systems are tightly linked, is a first step towards practicing more inclusive and sustainable land management. Social Ecological Systems (SES) are dynamic and complex landscapes, affected by economic, socio-cultural and biological factors (Alessa, Brown & Kliskey 2008). In their 2008 paper, Alessa et al seek to establish a strategy for identifying and mapping areas where “multiple and diverse human values converge with biophysical values.” The authors use hotspot mapping, overlaying human value and biological value maps to identify areas of convergence, or hotspots, and thus areas that should receive greater attention from land managers.

This study took place on the Kenai Peninsula in Southern Alaska, a large region with a variety of landscape types. Similar studies have looked at more micro-level landscapes, such as urban forests, also using participatory techniques to elicit human value maps and overlay them onto biological value maps (Korpilo, Jalkanen, Virtanen & Lehvavirta 2018). Mapping SES using a GIS and statistical methods can be a valuable tool for land managers, but care must be taken to ensure the process is truly participatory and equitable. Researchers have highlighted a number of inclusive participatory planning methods, including tenure and resource planning, sense of place mapping, and traditional ecological knowledge mapping (Mclain et al 2013), all of which could be incorporated into land management processes like the one outlined in this paper to increase participation and enhance data collection.




This study used a seven-step methodology to analyze SES hotspots on the Kenai Peninsula in Alaska. The basic outline is as follows: mapping social space, mapping ecological space, analyzing hotspot size and structure, correlating social and ecological space, and overlaying social and ecological space.


Mapping Social Space

Researchers elicited perceived human landscape values via a participatory process. The region was divided into 6 sub-samples based on the nearest community. Researchers analyzed 14 values: aesthetic, biological, cultural, economic, future, historic, intrinsic, learning, life sustaining, recreation, spiritual, subsistence, therapeutic, and wilderness. Participants received a questionnaire, a map of the region, and a dot sheet with colored dots that corresponded with each value. Six dots were provided for each value, and each dot represented a different numerical value from 0-50. Participants could identify which value they ascribed to a place and quantify how much they valued that place in comparison to other places. The resulting maps were digitized into a GIS and mapped to create weighted point density surface. This provided researchers with a series of social value hotspot maps that highlighted the most important landscapes in each community. This type of participatory process is valuable, but care must be taken to ensure all voices are heard and existing power imbalances are not reflected in the results. For example, the researchers did not address how they might reach out to individuals who are not English speakers, don’t have permanent addresses, or did not understand the slightly confusing and complicated mapping exercise.


Mapping Ecological Space

To map actual biological value the researchers used Net Primary Productivity (NPP), a measure of terrestrial vegetation growth, which can be a good indication of flora and fauna diversity. The researchers write that there are a number of potential parameters beyond NPP that could be used for mapping ecological space. A more robust study might use collected field data or combine multiple measures of biological and species diversity in order to build a more accurate map of ecological space.


Results: Spatial Correlations, Hotspot Analysis, Overlay of Social and Ecological Space

Three findings were highlighted. First, researchers looked at correlations in perceived landscape values between sub-samples. They found that higher valued landscapes tended to cluster around sub-sample communities, with the exception of larger areas of high regional importance such as Resurrection Bay. The authors also looked for correlations between perceived values of different communities, finding significant correlations between the landscape values of Anchorage and several other communities. This suggests the impact of “transient” communities within a region. Anchorage residents tend to travel regionally and might have a greater impact on natural landscapes than smaller communities (Alessa et al 2008).


Second, the authors analyzed the shape and mean size and number hotspots for each perceived value. Linear regression showed a strong negative relationship between number of hotspots and hotspot size. More “intangible” values, such as spiritual, were smaller in size and greater in number. More “tangible” values, such as recreation, were larger in size and fewer in number. This suggests efforts aimed at managing areas of high recreational value can be geographically broader, while efforts aimed at managing areas of high spiritual value should be more geographically discrete (Alessa et al 2008).


Third, the authors overlayed social and ecological value maps to find correlations and visualize SES space on the Kenai Peninsula. They found moderate correlations between perceived and actual biological value in 2 of the 6 sub-sample communities. The final overlay maps show “hotspots,” or areas of high perceived and actual value, “warmspots,” or low perceived value and high actual value, and “coldspots,” or low perceived and actual value. Hotspots should get more attention from land managers, assuming perceived value translates into actual use. Warmspots, being biologically productive areas where people are less likely to visit, might be important areas for conservation efforts (Alessa et al 2008).




This paper outlines an interesting and potentially actionable way for land managers to systematically analyze SES space. The statistical analyses yielded interesting results and suggest strategies for improving the land management process. If a process like this were put into action, I believe significantly more care should be taken during the data collection process. The authors state that one of the benefits of this process is that it can capture local and indigenous knowledge and make the land management process more equitable. This is only possible if researchers and land managers are intentional about their data collection, and thoughtful about how their data and results actually reflects the demographics, values, and knowledge systems of a particular community.



Works Cited


Alessa, N.A., Brown, G., Kliskey, A. A. (2008). Social–ecological hotspots mapping: a spatial approach for identifying coupled social–ecological space. Landscape and urban planning, 85(1), 27-39.


Korpilo, S., Jalkanen, J., Virtanen, T., & Lehvävirta, S. (2018). Where are the hotspots and coldspots of landscape values, visitor use and biodiversity in an urban forest? PloS one, 13(9), e0203611.


McLain, R., Poe, M., Biedenweg, K., Cerveny, L., Besser, D., & Blahna, D. (2013). Making sense of human ecology mapping: an overview of approaches to integrating socio-spatial data into environmental planning. Human Ecology, 41(5), 651-665.