For the next several posts I will talk about the process of method selection.
Method selection is tricky in general, but it seems particularly challenging when studying a complex socio-ecological system like a city, and a resource (P in my case) that is affected both by biophysical and by sociopolitical factors. I think it gets even more complicated because the methods you use also depend on the skill sets you have, collaborators and facilities you have access to, and what you want to learn. Obviously methods, first and foremost, need to be appropriate to answer the research questions.
In my case, I felt that the questions that interested me about the importance of local context on urban P cycling and the role of urban agriculture could require both natural science and social science methods. I wanted (and still want) to measure the flow of P in the urban ecosystem (natural science), and I wanted to understand the context that may be influencing the flow of P (social science). The division wasn’t that clear after all.
In order to carry out my research I selected the flowing strategies:
Case study comparison
I selected 6 cities, in fact 2 clusters of 3 cities, to be able to compare P cycling, UA, and P recycling between and within the clusters, getting a better idea of the importance of local context. Although case studies are used both in natural and social sciences, it seems that the literature on case studies, when you are involving people in the system (and cities do have a lot of people in them!) as a method is more developed in the social sciences ( I used this book as a guide).
Substance Flow Analysis
In order to compare P in the case studies I decided to use Substance Flow Analysis (SFA is used in both Industrial Ecology but also in Ecosystem Ecology, here are some papers that do it for N and P here, here, here, here, and a summary of the use of urban metabolism and SFA are here and here). It’s a method I am familiar with, as I used it in my master’s degree. I decided to do SFA on the food system of a city (not the city as a whole) to match my research questions and minimize unnecessary data collections (more on that in another post).
Basically for SFA of P you need to know:
- The boundaries of your system (physical, temporal, and theoretical)
- The amount of organic material and fertilizer (entering the city, being moved around in the city, and exiting the city as waste, pollution, or as a productive output)
- The amount of P in each one of these flows
In order to get these data you could
- Look at the peer-reviewed literature and official city, organizations, or company documentation
- Collect secondary data by asking stakeholders (people that participate in the food and waste system of the city) through surveys and interviews about what they know about the flow of these materials and P concentrations
- Collect primary data by going around and collecting your own data with field measurements and lab tests (both flow of material and the concentration of P).
In order to examine the effect of “local context” it seems that you first have to define what local context is in the framework of your questions (more on a post about what am I really measuring). I see three broad ways to look at it.
- Literature review on studies where their explicitly interviewed people about why they do what they do.
- Review of “larger scale” official data about the city as a whole. For example, temperature, socio-demographic data or survey data on practices carried out by cities or other organizations.
- Collect your own interview and survey data about motivations, barriers, and facilitators.
I think it’s important to note that these methods are not mutually exclusive and that in order to triangulate bias (jargon word that means trying to better understand a system or event from more than one perspective) you need to use more than one method. It also important to note that not all these methods measure the same thing, and that taking into consideration limited financial and time resources excludes some of these methods from being “fully” carried out.
New York City (left) and Montreal (right), two of my case studies cities, on a cloudy day. They have some similar local context (both islands), but are also different. Looking at P cycling and UA will be exciting in both cases!