Service Learning, Challenges in Open Mapping for Resilience

How will we work together to create data for humanitarian and development needs?

This course is organized where you will be working on teams. Everyone knows that group work is sometimes fun and sometimes can be not so much fun. It is important that your group functions as a team. Read the section of Development of a Group, Chapter Four from the top of page 54 to the end of page 60 (skip the callout boxes) to discover some ways to facilitate the development of your group and the success of your project. Refer to the checklist on page 75, exercise 4.9 as we will go over this list in class.

Another aspect of your service learning experience will be to engage with a YouthMappers chapter in the country where your project is taking place. Creating these cultural connections is not always easy or straightforward, especially when done virtually and remotely. It is important to get a solid understanding of how our mindsets may affect our work, and our collaborations. Read the section on Building Intercultural Sensitivity, Chapter Five from the bottom of page 81 to the end of the section on page 85 (the stages) and reflect on some of the unique ways that online volunteer mapping relate to difference, culture, power, and privilege.

Technical Challenges in Open Mapping for Resilience

Now, let’s review and explore in greater (academic) detail a couple of the challenges that we should be aware of when working on open, volunteer platforms aimed at addressing resilience in real-world development contexts: On the technical side, the accessibility, quality, reliability and validity of crowd-sourced or voluntary geographic data that is produced at quantities needed are important considerations. On the analytical side there is the issue about how knowledge is created from this data, both in terms of the process of moving from data to decisions, as well as what we learn as mappers from the process of engaging in these endeavors.

Quality versus Quantity of Open Data

Public participation in spatial data creation has empowered citizens to provide knowledge and context to open map resources (Goodchild 2007). While these technological advances have permitted the amount of available data to grow immensely, concerns remain regarding the quality, reliability, and veracity of crowd-sourced or volunteered geospatial information (Heipke 2010).

Scholars have noted that the quality of crowd-sourced data can be inferior to data collected using traditional scientific means. It contains errors, such as in georeferencing, feature definition (topology), and attribute type (Jackson, et al 2013; Elwood, Goodchild & Sui 2013). Volunteered geographic data and other type of crowd-sourced data are also frequently collected without adherence to metadata standards, and irrespective of local knowledge, therefore hampering its credibility (Flanagin & Metzger 2008). Furthermore, concerns about the veracity of spatial data collected by volunteers include its completeness (Jackson, et al 2013), unreported contributor bias (Neis & Zipf 2012), and lack of accounting for the social and political practices implied in creating spatial data (Elwood 2008). Similar to traditional geospatial data collection, the quality of VGI can be framed using the following 5 dimensions: positional accuracy, attribute accuracy, logical consistency, completeness, and lineage (Goodchild & Li 2012). Perspectives from the development community resonate with this critique (Weingarten 2010). Currion (2010) has criticized the unreliable nature of crowd-sourced data in times of crisis, and questions the valued added to disaster response efforts. Some decision-makers are reluctant to utilize volunteered and crowd-sourced data due to its lack of official stature. Such concerns have resulted in research to assess and assure the quality of VGI (e.g., Haklay 2010; Haklay, et al. 2010; Barron, Neis & Zipf 2014) and to integrate the often unstructured location-based social media (e.g., Twitter) with GIS and analysis environments (Cao, et al. 2015; Wang, et al. 2013). In particular, Goodchild and Li (2012) provide a set of general guidelines to ensure data quality. Ballatore and Zipf (2015) present a conceptual quality framework for VGI with a particular focus on the conceptual compliances. The quality of OpenStreetMap (OSM) has been intensively evaluated, as one of the leading VGI projects in the world (Mooney & Corcoran 2012). Comparative studies with authoritative data sources (Haklay, et al. 2010; Zielstra and Zipf 2010) have shown high quality variability across rural and urban areas.

Analytical Challenge in moving from Data to Knowledge and Decisions

Even with a robust conceptual framework using the highest quality, complete data, the process of moving towards better decision-making is not automatically guaranteed. Despite increasing amounts of readily available data, or perhaps because of it, a myth has emerged that having data, particularly large data sets, offers better or more advanced knowledge than was previously impossible, and this leads uncritically to novel insights based on accuracy and objectivity (boyd & Crawford 2012). This persistent misconception is likely due in part to the prevailing idea that an uninterrupted chain exists leading from data to information to knowledge to wisdom. The Data-Information-Knowledge-Wisdom hierarchy has its early notions attributed to geographer Yi-Fu Tuan (Cleveland 1982), and engineer Mike Cooley (1980) in his critique of computerization (Sharma 2008; Ackoff 1989). Now taken for granted, the hierarchy has been abbreviated by proponents of the Data Revolution who give short shift to the process, or conflate the chain altogether, simply as Data to Decisions (UN 2014; Stuart, et al. 2015). This model also has the propensity to gloss over missing data, to imply filtering (Weinberger 2010); to assume simple inductivism (Frické 2009); and to lead to misinformation (Burns 2008). As Cowell and Lennon’s meta-analysis shows (2014:263), “actively cultivating wide stakeholder buy-in” to new approaches “may secure wider support, but not necessarily translate into major influence on decisions”. In the end, this understanding should be grounded in context, uncertainty, and reality (Kates & Burton 1986; Gober, et al. 2010; Hermann & Neumeier 2008). In practical terms, this means we should in advance of mapping, thoughtfully consider the explicit analytical processes by which open spatial data can influence development decisions, taking advantage of both the cross-sectoral and international perspectives.

Finally, what kinds of knowledge can mappers themselves gain by participating?

Read all of this awesome article: What Happens When Everyone Makes Maps?

What kind of Leader is needed to meet these challenges?

Finally read about the Leadership Toolkit in Chapter Eight of our book, just pages 127-132 to gain some insights on various kinds of leaders and how they impact community projects. Think about when you may have served as "director, connector, or persuader."

RELFECTION QUESTIONS FOR YOUR ESSAY

Q: What can we do to ensure appropriate quality control of open spatial data for our class projects?

Q: What do you think the author of the linked article means when saying “the way we draw the map actually changes the thing that we’re mapping” ?

Q: What do you personally hope to learn, particularly what knowledge about the world might you want to gain, by mapping openly for resilience internationally?

Q: What kind of leaders are needed to address the special kinds of challenges that the open data community face, and how can each contribute?

Q: What kind of leader are you?

Q: What is your best advice for what a group should do to work together well? How can we make sure each member does a fair share of the tasks? What agreements will your team put in place to deal with conflict or groupthink?

Q: What can be done to minimize misattribution?

Q: How might power, marginalization, discrimination, and privilege affect the relationships among your class team members? How might they affect the international collaboration with the YouthMappers chapter in your project country?

Q: How is service learning different from other forms of learning? What do you anticipate will be the challenges of this type of course? What do you anticipate will be the unique benefits of learning through serving for you personally?

Q: What is the difference between charity and solidarity?

USE THE "Add files" icon below to upload your essay as a word doc or pdf. The filename should be your last name and the word "Service." For example, SOLISservice.pdf.