There is a science
that defines sustainability. At times there may also be a legal framework to define
the effort. To make sustainability happen, both the scientific foundation as
well as the legal framework must be accompanied with a community effort.
Community can be defined in many different ways, but what all definitions have
in common is the need to secure diverse modes of participation, to provide a
flexible organizational structure, and to provide a collaborative data
environment that bring all elements together. Without a community behind it,
any goal is difficult to achieve, despite legal or scientific support. In this
short article I will summarize some of our experiences gained from developing a
collaborative data environment called myObservatory (or in
short, mO; see myobservatory.org), and from dialogues with our partners and users.
Let’s start with community. Communities could vary in membership. A
community may include a few cattle farmers in Argentina, or it could scale up
all the way to national organizations of citizen scientists, like the ASPEA
organization in Portugal (as discussed here), which monitors the health of the national river network with tens of thousands
of kilometers and thousands of volunteers. Community can even be a global
organization that supports sustainable agriculture like Savory Global. Communities both large and small need advanced collaboration tools. The level
of sophistication should not depend on the scale of the institute. This is what
guided our thinking in designing our collaborative data environment.
How do we make communities, small or large, work together? There
may be different factors to consider. Obviously, a compelling vision is needed
in order to attract participation and to maintain enthusiasm. To translate this into action, a
collaborative data environment is needed, one that would allow the community to
translate motivation into tangible products, and to do so, (1) it must provide
the organization with flexibility in defining and accommodating user roles; (2)
it must accommodate multiple and diverse modes of data entry, and (3) it should
be able to maintain the credibility and integrity of the data collected, and
(4) it should allow users to generate meaningful and exciting content. It must
also be easy and pleasant to use, otherwise the tools themselves serve as a
barrier to achieving the goal. Let’s take a look at some of these
elements.
User Roles. A collaborative data environment needs
to accommodate a wide range of roles, such as administrators, technicians,
consultants, citizen scientists, analysts, observers, and possibly others. A
user role is not just a title. A user is defined by data access privileges, and
by the options to create content and provide guidelines.
Occasional users, outside of the core group of committed
users, should also be accommodated. We would like to have a core group of professionals
and volunteers, providing support on a regular basis. But we should also
appreciate the occasional visitors who may want to inspect our work or make
occasional contributions. This is
especially true when reports on hazards or special events are welcome and
encouraged. People with smartphones could provide a huge source of actionable
information, especially when immediate action is warranted. Imagine a user
taking a picture of some environmental hazard using a smartphone, which,
geotagged and dated, is then immediately wired to immediately become a part of
the database and a GIS display, and generating some sort of response. This accessibility
empowers communities to take charge over their environment, and could keep the
entire group fired up and motivated.
Collaborative efforts in support of environmental sustainability
operate in a multi-stakeholder environment. This requires careful planning. I
mentioned already the strict user access controls intended, among other things,
to protect privacy of data. Operating of the data environment by a private
entity which is not subject to Freedom of Information Act (FOIA) could be very
important, as many stakeholders may be willing to share data under limiting
conditions that cannot be met by government agencies, thus preventing the fear
of bad publicity from deterring remediation efforts.
We also realized that not all of those who wish to use data as
participants are equally motivated. What we found, however, is that in some cases
users could be enticed to participate and become full-fledged contributors by being
able to benefit from the collaborative data environment in ways that may be indirectly
related to sustainability. Drawing from
our experience in a project focused on sustainable management of a groundwater
basin in California, for example: here the list of stakeholders included state
and local water agencies, as well as private well-owners and volunteers. Private well owners proved to be a challenge,
as they are not required, by California law, to provide data on their wells. However,
we managed to make some progress here by providing benefits that are directly
related to groundwater such as updates and alerts related to regional and local
trends in groundwater levels. Additionally, we provided access to agriculture related
functions that are indirectly related to groundwater (e.g., degree-days needed
for pesticide management, tools for analysis of pumping tests). For this, we
implemented a wide range of analytical and scientific tools, all mounted on a
GIS platform. For example, consider this image which provides a snapshot of groundwater
levels and flow directions at the Sonoma Creek Groundwater aquifer in
California. This map is generated in
real-time from data provided by all the stakeholders. The green dots represent groundwater
state-owned monitoring wells. The blue lines represent groundwater levels and
the red arrows mark flow directions. Looking at consecutive snapshots like
this, one could draw conclusions about trends. Well owners are particularly
worried about the water level falling below critical elevations required by
their pumps. Addressing this and similar concerns could be very useful in
attracting participation.
Multiple Data Entry
Modes. Flexibility in
defining metrics for sustainability requires having multiple modes of data
entry. This could include manual data entry into specially designed forms, file
uploads and sensor feeds. All data should be geo-tagged and dated. Editors
should be allowed to fill in the blanks with any data that is not geo-tagged.
Smartphones
are particularly useful for connecting with a large number of users in
real-time. In mO, we view the smartphone as a vital component, and to
accommodate it, we created seamless connectivity between smartphones and
desktops. Our smartphone technology allows quick and seamless assimilation of
data. Data transmitted via smartphones includes pictures, notes, and filled-in
forms, and it becomes actionable as soon as it is being transmitted and
displayed, which could take a fraction of a second. For example, in our Natuf
Project in the Middle East, users record information on new environmental hazards
by filling in specially-designated forms and taking notes and pictures. This
information is transmitted and as soon as received it is used to update a
hazard map in real-time. This map is then processed together with a
vulnerability map using a built-in algorithm, producing an updated risk map. This
process is demonstrated in the Figure below. The vulnerability map represents
local conditions (soil, vegetation, water resources, land use, depth to
groundwater, and others). The map at the center represents recorded hazards.
The multicolored icons mark where risk were reported. These icons are
clickable, to reveal all the relevant information. Each icon point contains a description of the pollution hazard and
perhaps even a picture, which can be viewed on top of the risk map to
understand what is causing risk in an area.
The left and center figures
above represent intermediate output maps, generated by interpolation from point
sources of hazard and risk data. These two maps are then processed to produce
the risk map, shown on the right. The risk map updates daily. It is a vital tool for maintaining
the sustainability of the underlying aquifer. This entire process is executed using scientific modules embedded in mO. The complete
user-smartphone-desktop process is shown and discussed here.
Maintaining
Data Integrity. I am not talking here about data quality. This is a
separate issue, which can be addressed in different ways, e.g., by training and
verification. What integrity means is adhering to well-known practices of data
custody. That requires maintaining strict user access controls and maintenance
of a chain of custody. Site
administrators should be given the necessary tools to maintain the integrity of
the data, and required to follow strict data protocols governing data and what
can be done with it.
Content and Analytics:
Making Sense of Data. This is the most rewarding component of the myObservatory collaborative
data platform. Ideally one should just be able to analyze the data as it flows
in, identify trends, generate or receive alerts, communicate with stakeholders,
compare results across regions, identify data needs, and manage volunteers, all
by clicking on an icon (preferably on a map). To make this happen, we
implemented analytical tools that are used universally. We also realize that
different organizations have different needs, and so we have the option to
implement project-specific or even proprietary tools.
Summary. What I presented here is a short summary of an environmental
information management system, tailored for the need of sustainability. It was
developed by a group of engineers, programmers and sustainability experts. It
evolved over many years of experience and user interaction. Give it a try and
let me know what you think so that we could improve our technology. Get in
touch with myObservatory if you need any assistance getting started, or want to
discuss using it as a potential solution to a problem. Or, just post a comment
or a question here!
