The AIANY invited me to present my perspective on Occupy Sandy at their event “Stand Up! How to be Part of the Solution after a Disaster.” My presentation argues that Occupy Sandy, and the mutual aid work of its predecessor Occupy Wall Street, were physical-world manifestations of the “Open Aid” trend taking place in the disaster relief and humanitarian aid sectors.
The presentation begins by pointing to the fact that “faith in institutions” is at an unprecedented low in the USA at the same time as our economy is being transformed by widespread access to networked communication technologies. These technologies enable autonomously organized, local grassroots disaster response efforts to network with each other to create a new type of entity that the Department of Defense is calling “Grassroots Disaster Relief Network” (GDRN). In the virtual world, networked communication technologies are also allowing people with specialized technical skills to organize themselves into groups that can provide information processing services through a wide variety of tools including social media, GIS and collaborative documents. These groups are called Volunteer Technical Communities (VTCs).
I argue that GDRNs are a local/physical manifestation of the “Open Aid” concept, and VTCs are a global/digital one. Currently VTCs tend to serve formal response organizations such as UNOCHA, but in the not-too-distant future they’ll be able to collaborate directly with GDRNs, giving disaster survivors and their communities unprecedented access to information.
The presentation ends with some suggestions for how we can set up simple, open source systems to streamline information flows related to disasters.
I gave a very similar presentation to disaster response personnel at the Disaster Preparedness Exchange in Indianapolis a week later.
This conference was my first time engaging, in-person, with the disaster relief community outside the United States and I was extremely impressed. Unlike most conferences I attend in which there is an abstract “theme” with random and broad sessions, this conference had a laser-like focus on a very specific data standard called the Common Alerting Protocol (CAP). The goal of this protocol is to facilitate in the exchange of “all-hazard emergency alerts and public warnings over all kinds of networks.”
Presentations and discussion were focused on the design and implementation of this specific data standard. There were also sessions organized in which stakeholders worked together to create field-by-field recommendations for how to improve future versions of CAP. The amount of information that was shared and the effective collaborations that took place were inspiring.
We need many many more events that are focused exclusively on the design and implementation of data standards within the disaster relief and resilience community. If we can come together to create a shared language and set of data standards for our work, then information sharing will become radically easier. Easier information sharing leads to better situational awareness, more efficient resource distribution, and more positive outcomes.
I’m look forward to bringing some of the the tools and techniques I learned at this event back with me to the USA.
Immediately after a disaster, information managers collect information about who is doing what, where, and turn it into “3W Reports.” While some groups have custom software for collecting this information, the most popular software tool for this work is the spreadsheet. Indeed, the spreadsheet is still the “lingua franca” of the humanitarian aid community, which is why UNOCHA’s Humanitarian Data Exchange project is designed to support people using this popular software tool.
After those critical first few days, nonprofits and government agencies often transition their efforts from ad hoc emergency relief and begin to provide more consistent “services” to the affected population.
The challenge of organizing this type of “humanitarian/human services” information is a bit different than the challenges associated with disaster-related 3W reports, and similar to the work being done by people who manage and maintain persistent nonprofit services directories. In the US, these types of providers are often called “211” because you can dial “211” in many communities in the US to be connected to a call center with access to a directory of local nonprofit service information.
During the ongoing migrant crisis facing Europe, a number of volunteer technical communities (VTCs) in the Digital Humanitarian Network engaged in the work of managing data about these humanitarian services. They quickly realized they needed to come up with a shared template for this information so they could more easily merge data with their peers, and also so that during the next disaster, they didn’t have to reinvent the wheel all over again.
Since spreadsheets are the most popular information management tool, the group decided to focus on creating a standard set of column headers for spreadsheets with the following criteria:
To create this shared data model, we analyzed a number of existing service data models, including:
Stand By Task Force’s services spreadsheet
Advisor.UNHCR services directory
Open Referral Human Service Data Standard (HSDS)
The first two data models came from the humanitarian sector and were relatively simple and easy to analyze. The third, Open Referral, comes from a US-based nonprofit service directory project that did not assume that spreadsheets would be an important medium for sharing and viewing data.
To effectively incorporate Open Referral into our analysis, we had to convert it into something that could be viewed in a single sheet of a spreadsheet (we call it “flat”). During the process we also made it compliant with the Humanitarian Exchange Language (HXL), which will enable Open Referral to collaborate more with the international humanitarian aid community on data standards work. Check out the Open Referral HSDS_flat sheet to see the work product.
We’re excited about the possibility that Open Referral will take this “flat” version under their wing and maintain it going forward.
We hope the HSDM will be used by the various stakeholders who were involved in the process of making it, as well as other groups that routinely manage this type of data, such as:
member organizations of the Digital Humanitarian Network
grassroots groups that come together to collate information after disasters
big institutions like UNOCHA who maintain services datasets
software developers who make apps to organize and display service information
I hope that the community that came together to create the HSDM will continue to work together to create a taxonomy for #service+type (what the service does) and #service+eligibility (who the service is for). If and when that work is completed, digital humanitarians will be able to more easily create and share critical information about services available to people in need.
* Photo credits: John Englart (Takver)/Flickr CC-by-SA
Over the last year, a number of clients have tasked me with bringing datasets from many different sources together. It seems many people and groups want to work more closely with their peers to not only share and merge data resources, but to also work with them to arrive at a “shared data model” that they can all use to manage data in compatible ways going forward.
Since spreadsheets are, by far, the most popular data collection and management tool, using spreadsheets for this type of work is a no-brainer.
After doing this task a few times, I’ve gotten confident enough to document my process for taking a bunch of different spreadsheet data models and turning them in a single shared one.
Here is the 10-step process:
Create a spreadsheet. First column is for field labels. You can add additional columns for other information you’d like to analyze about the field such as its data type, database name and/or reference taxonomies (i.e. HXL Tag).
Place the names of the data models you’ve selected to analyze in the column headers to the right of the field labels.
List all the fields of the longest data model on the left side of the sheet under the “Field Label” heading.
Place an “x” in the cells of the data model that contain the field to indicate it contains all the fields documented in the left hand column.
Working left to right, place an “x” to indicate when a data model has a field label contained therein. If the data model has that field but uses a different label, place that label in the cell(4a). If it doesn’t have that field, leave the cell blank. Add any additional fields not in the first data model to the bottom of the Field Labels column (4b).
Do the same thing for the next data models.
Once you have all the data models documented in this way, then you can look and see what the most popular fields are by seeing which have the most “x”s. Drag those rows to the top, so the most popular fields are on the top, and the least popular fields are on the bottom. I like to color code them, so the most popular fields are one color (green), the moderately popular ones are another (yellow) and the least popular but still repeated fields are another (red).
Once you have done all this, you should present it to your stakeholder community and ask them for feedback. Some good questions are: (a) If our data model were just the colored fields, would that be sufficient? Why or why not? What fields should we add or subtract? (b) Data model #1 uses label x for a field while data model #2 uses label y. What label should we use for this and why?
Once people start engaging with these questions, layout the emerging data model in a new sheet, horizontally in the first row. Call this sheet a “draft template”. Bring the color coding with it to make it easier for people to recognize that the models are the same. As people give feedback, make the changes to the “template” sheet while leaving the “comparison” sheet as a reference. Encourage people to make their comment directly in the cell they’re referencing.
Once all comments have been addresses and everyone is feeling good about the template sheet, announce that sheet is the “official proposal” of a shared data model/standard. Give people a deadline to make their comments and requests for changes. If no comments/changes are requested – congratulations: you have created a shared data model! Good luck getting people to use it. 😉
Do you find yourself creating shared data models? Do you have other processes for making them? Did you try out this process and have some feedback? Is this documentation clear? Tell me what you’re thinking in the comments below.
“The software revolution has given people access to countless specialized apps, but there’s one fundamental tool that almost all apps use that still remains out of reach of most non-programmers — the database.” AirTable.com on CrunchBase
Database technology is boring but immensely important. If you have ever been working on a spreadsheet and wanted to be able to click on the contents of a cell to get to another table of data (maybe the cell has a person’s name and you want to be able to click it to see their phone #, photo, email, etc), then you’ve wished for a DIY database.
I’ve been waiting for this technology for many years and am happy to report that it’s nearly arrived. Two startups are taking on the DIY database challenge from different sides:
Awesome-Table is a quick and easy tool for creating visualizations of data inside Google Sheets. It offers a variety of searchable, sortable, filterable views including tables, cards, maps and charts. They’re easy to embed so they are great for creating and embedding directory data onto websites. Here’s an awesome table visualization of worker coops in NYC.
AirTable is a quick and easy way to create tables that connect to and reference each other. This allows for multi-faceted systems you can travel through by clicking on entities. For example, you can define people in one table, organizations in another, and offices in a third, and then connect them all together so a user can browse a list of people, click on an individual’s organization, and then see all that organization’s information, including its many offices. Pretty useful!
The progress of these two startups leads me to believe we’re less than a year or two away from truly lightweight, easy to use, free of cost, DIY database building systems, and an open source one not too long after that.
The increasing accessibility of database technology has a lot of implications. The most obvious one is that it will enable people to build their own information management systems for common use cases like contact directories, CRM systems and other applications that just can’t be done with existing spreadsheet technology. This will make a wide variety of solutions more accessible to people – so if you want to start or run a business, manage common information resource, or just organize personal information better, you’ll enjoy DIY databases very much.
More interesting to me is the implication that they can have for people trying to reform and democratize institutions.
If you spend time in the type of information management systems used by institutions big and small – whether it’s government agencies like the sanitation department or educational ones like high schools or universities, you’ll quickly notice that many of their most useful and critical tools are nothing more than a set of data tables (directories) and visualizations of the data contained therein (search/filterable tables, cards and maps of that data.)
These very rudimentary but widely used internal software systems not only define the information people within that institution can access and share, but also limits them to very specific workflows that are implicitly or explicitly defined in the software. Since workflows define the work people actually do, the people who control the workflow are also people who control the workers.
If you want to change how an institution does things, you have to be able to change its information management systems. Since current database technology requires specialized software coding skills, changing these systems often turns into a bureaucratic nightmare filled with bottlenecks. First, a specific group of pre-approved people need to agree to design and fund a change, then another specific group of people need to program and implement the change, and yet another group is often tasked with training and supporting users who then have to use the updated system. That creates a lot of potential bottlenecks: executives who don’t know a change is needed or don’t care enough to fund the work; managers who don’t want to get innovated out of a job or don’t know how to design good software; technologists who don’t have the time to implement a change or don’t have the motivation to do the job right. With all those potential bottlenecks it’s easy to see why so many well funded institutions have such crappy software and archaic workflows.
When people try to improve institutions, they are often trying to improve workflows so more can get done with less time and resources. Unfortunately, the people who actually know what changes need to be made are rarely in a position to control the architecture of the databases they use to get things done.
With DIY databases, people within institutions can circumvent all these bottlenecks simply by making superior systems themselves. This can change a lot more than simply the type of information people have access to – it allows them to explore news ways of being productive. What they’ll inevitably discover, particularly if they’re in an institution that spends a lot of time managing information, is that they can do a better job managing information than many of their bosses.
DIY databases are enabling the type of horizontal and bottom-up innovation essential not just for better functioning institutions, but also more democratic ones. Databases are the “means of production” for many information workers. When they can build and own their own ones, they’ll be able to achieve more ownership of their own work and take another big step towards being able to manage themselves.
Of course, as technology improves and creating you own databases becomes easy, the hard part will certainly become getting peers to use them. That’s a topic for another day.
The International Association of Emergency Management (IAEM) Conference was described to me as the Oscars of Emergency Management field. The event took place in the Paris Hotel in Las Vega November 14th. It was three days after the Paris attacks. Walking under the hotels faux Eiffel Tower and through its simulated Parisian streets was uncomfortable.
Nevertheless, the event was quite informative. Right before my presentation was a session about building your own emergency operations center (EOC) with inexpensive off the shelf tools and another one by the head of St Louis’s Office of Emergency Management explaining how he managed the reaction to the killing of Michael Brown.
My presentation wasn’t as well attended as I had hoped. Maybe the title wasn’t compelling. But it went well. The audience was engaged and we had a good back and forth. Unfortunately, due to technical problems, my sessions wasn’t recorded like all the others. I would have really liked to have seen that video. Instead, at the request of the IAEM, I recorded my presentation via Hangout Live. You can see that video here.
This presentation is the most well rounded of them all. It gives a solid overview of the four facets of open aid:
Grassroots Disaster Relief Networks
Volunteer Technical Communities
At the end it offers a diagram for how we build an integrated information management ecosystem cycling information from local community groups through municipal, state and federal agencies and channel resources effectively.
In the wake of Superstorm Sandy, many residents of New York City were left struggling.
Though a broad array of supportive services were available to survivors — from home rebuilding funds to mental health treatment — it’s often hard for people to know what’s available and how to access it. New York City lacks any kind of centralized system of information about non-profit health and human services. Given the centrality of non-profit organizations in disaster relief and recovery in the United States, this information scarcity means that for many NYC residents recovery from Sandy never quite happened.
As in any federally-declared emergency scenario, every officially-designated disaster case management program was mandated to use the same information system — the Coordinated Access Network (CAN.org) — to manage survivors’ access to benefits and other steps along the path to recovery. CAN has its own resource directory system, but it is proprietary and not available to the public; survivors often need to make a phone call to a case manager to get even the most basic information about the services. In conversations with those case managers who have had the privilege of being able to access this resource, we’ve heard that its interface is confusing and its data is often duplicated and outdated.
As a result, most disaster case management agencies ended up managing their own resource directories — in isolation from each other. Some organizations were able to cobble together relatively comprehensive service directories, but others don’t have any, and rely on individual case managers to solve the problem themselves. Now, just a bit over two years after the storm, the funding for these disaster case management programs is coming to a close — and so the local, personal knowledge about Sandy recovery services held by these social workers will disappear.
The data in our directory comes from a hodgepodge of sources: nonprofit websites, PDF printout, shared spreadsheets created by long term recovery group members, and .CSVs produced by individual case managers passionate about sharing resources. Initially, we used Google Spreadsheet and Fusion tables to manage all of this.
With the introduction of the Human Services Data Specification (HSDS), through the Open Referral initiative, we’re now able to manage this information using a standardized, well documented format that others can also use and share. And that’s precisely what we try to encourage others to do.
Openly accessible, standardized human service directory data is critical for each of the phases of a disaster. For disaster preparedness, service information can help identify gaps in the allocation of resources that communities might need during a disaster. For disaster response, many different kinds of organizations and service providers need simultaneous access to the same information. For disaster recovery, survivors need an array of services to get back on their feet, and they should be able to find this information in a variety of ways.
With the Ohana API, we can glimpse a world in which all of the needs above can be met. So we’ve deployed a demonstration implementation of Ohana at http://services.nycprepared.org. In Ohana, we now have a lightweight admin interface for organizing our data and a front-end application to serve it to the public in a beautiful and mobile friendly way. Since Ohana is an API, other developers can use it to make whatever interfaces they please.
While we’re quite impressed with the Ohana product, its out-of-the-box web search interface won’t meet everyone’s needs. The system that we’d most like to use would be our open source disaster management software called Sahana. Sahana is the world’s leading open source resource management software and we want to build a component — available to any community — that will enable it to consume, produce and deliver HSDS-compatible resource directory data.
By making it possible for any agency using Sahana-based systems to consume and publish resource directory data in the Open Referral format, we can shift the entire field of relief and recovery agencies towards more interoperable, sustainable, and reliable practices. Sahana specialists are ready to develop this open source, HSDS-compatible resource directory component — at an estimated cost of $5,000. Please consider donating to our effort. And please reach out to Sarapis if you know of other communities and use cases in which this technology could enhance resilience in the face of crisis.
This presentation was delivered at the National Voluntary Organizations Active in Disaster Conference 2015 in New Orleans.
I’ve been an active (and actively marginalized) participant in my local NYCVOAD community, so it was nice to feel accepted by the broader VOAD community.
Of all the presentations I’ve given, this one felt the best. The audience was very engaged and we had a robust back and forth. It felt electric. Outbursts came from the audience. It felt like a unique space. The feedback was fantastic. Much thanks goes to Marie Irvine who helped put the presentation together and who co-presented with me.
This presentation is based around the concept that “Open Networks that efficiently provide relief after a disaster are built on Open Technology and Open Data. It explains NYC:Prepared’s toolset and has extensive training materials about open data within the context of disaster.
The establishment, at least a very small subset of it, discovered my work the second week of October. It wasn’t a thunderstorm of interest — more like scattered showers — but when you’ve been in the desert for a while, a little rain can go a long way.
“The Christian Regenhard Center for Emergency Response Studies (RaCERS) is a unique applied research center focused on documentation of lessons learned and planning for future large-scale incidents.”
I had the honor of presenting to one of their classes of students pursuing masters degrees in Emergency Management as well as a number of professors in the school.
This presentation was very similar to the one at the IEEE HTC Conference a few days earlier, but since it was to a New York focused audience, I explored the connection between Occupy Wall Street and Occupy Sandy a bit more extensively.
The audience reaction was extremely positive. The professors and students asked a ton of questions and everyone expressed frustration with the state of information sharing in the Emergency Management sector. There was one older man who mean mugged me the entire presentation, had no questions and didn’t say a word. I couldn’t tell if he was upset with me for arriving late (sorry!) or because he really didn’t like the way I presented Occupy Wall Street as an important element in the resilience of New York City.