Connecting data points through time (or not)

Answering the question “who was where when?” is central for investigations into allegations of human rights abuse(s). Because of this perhaps one of the most defining, and complicaticating, features of the Security Force Monitor’s data is that almost everything we research is connected to time including:

  • Existence of units
  • Parent relationships between units
  • Location of units
  • Areas of operation for units
  • Membership/participation of units of in multi-unit operations
  • Positions held by people

While attaching time to data points aids our mission to support human rights investigations and advocacy, it raises methodological challenges. The Security Force Monitor has developed a methodology to address the issue of time which this blog will lay out in detail including:

  • Why the Monitor would (or would not) connect two bits of data through time
  • How the Monitor handles gaps in the public record
  • Questions analysts run through while reviewing time based information

As always – questions, comments and any other feedback is welcome!

Fragmentary nature of time in sources

In an ideal world the Monitor would have a source from every day of the year stating where a unit was located or conducting operations. Barring that having multiple sources regularly making statements like “since X date this unit has been based in this city” would be tremendously helpful. Unfortunately, neither scenario currently occurs, or is likely to occur in the near future, making it necessary to develop a robust way of thinking through time.

Broadly speaking the Security Force Monitor uses agreement among sources to build up details on security force units and individuals. Most of the Monitor’s sources, like government press releases and newspaper articles, can be used to link a value, such as the location of a unit, to a specific date (usually the date of publication). As we collect more sources we need to determine what agreement among sources means for time based values, like the location of a unit.

Connecting through time or not

Example: the Monitor comes across Source A published on 1 July 2012 stating that the 1 Battalion is based in Lagos. If Source B published on 3 August 2012 also states that the 1 Battalion is based in Lagos we have a decision point about what claim we should make.

Utilizing sources A and B we have two options which can be expressed in text:

  1. Separate claims: “As of 1 July 2012 the 1 Battalion was based in Lagos and as of 3 August 2012 the 1 Battalion was based in Lagos, the Monitor does not know where the battalion was based between those two points in time.”
  2. Contiguity claim: “From at least 1 July 2012 to at least 3 August 2012 the 1 Battalion was based in Lagos.”

Thus, whenever the Monitor gets a new source of information we have to decide whether to make a “separate” or “contiguity” claim. Based on the example of the 1 Battalion above the Monitor would run through a series of questions to determine which claim (if any) to make:

  • In general, how do other battalions operate, are they sedentary, or highly mobile?
  • How has the 1 Battalion acted in the past, has it been sedentary or highly mobile?
  • Are there other sources disputing these claims (i.e. 1 Battalion being based solely in another city)?
  • Are there any sources indicating the 1 Battalion was in Lagos in July and/or August as part of a “special”, “emergency” or otherwise temporary posting?
  • Are there sources that indicate the 1 Battalion moved in between these two points of time and thus these should be treated as separate deployments to Lagos?
  • Is there anything related to the 1 Battalion’s parent or child units that may impact where it was based?
  • Are there any other mitigating sources (i.e. major restructuring of the military, constitutional changes, etc.) which may impact the basing of the unit?
  • Is more research needed before the Monitor can make any claim?

An argument could be the Monitor should always make “separate claims” as that would be more faithful to the sources. However, the result likely mean an almost incomprehensible amount of detail in the records of people and units, which would obscure when changes really did occur, for instance when a person changed positions or a unit ends operations in an area.

Perhaps the most important point is that it even though data points, like where a unit is based, can be continuous through time, it should never be assumed that those types of features remain consistent between two or more sources. Time is a constant challenge, but given that is a key element in identifying perpetrators of human rights abuses it is necessary to get it right.

WhoWasInCommand shows you all the sources that evidence every piece of data – but you probably missed the way it does this

WhoWasInCommand shows you all the sources used to evidence every piece of data it provides.

When you’re browsing your favourite units and commanders on – like Operation Lafiya Dole, for example –  just hover your mouse over (or tap on, if you’re on a mobile or tablet) the bit of data you’re interested in and this happens:


This interaction gives you a lot of useful information:

  • Because the little circle is green, it tells you that we have rated this bit of data as “High Confidence” (which means it is drawn from a wide variety of sources of different types)
  • The pop-over that appears when you click tells you how many sources there are
  • You can scroll to see them all the sources, along with links to the source’s URL (even if it’s now dead) and a link to a copy of the source we made by submitting it to the Internet Archive
  • The little question mark icon links off to the page in our Research Handbook that answers questions about this widget.

Now, I think this feature is pretty cool (well, I designed it so I would say that). We did some  research into how citations, references and footnotes were managed on websites, and our hunch was this would be a good start.

But it’s not my view that counts – it’s your view as a user that matters.

We get a lot of questions about our sources and whilst it’s clear this feature is a practical way to deliver information that answers those questions, I suspect that a lot of users don’t use it either because it’s not immediately apparent it is there or because it is not how users would think about how to find sources.

We could do to sit down with people who are using WhoWasInCommand, watch how they use the site, and ask them for ideas about how we can make these sorts of features clearer.

Any volunteers?




OpenStreetMap is (sometimes) a handy database of military and police locations – here’s how to see them

OpenStreetMap – 70,641 objects are tagged with “landuse=military”. Source: TagInfo, 6 July 2018

Most of the time we use OpenStreetMap (OSM) as a gazetteer; that is, a means of representing the geographical aspects of Security Force Monitor’s data.

For example, our research indicates that the Mexican army unit 105 Batallón de Infantería had a base in Frontera, Coahuila, Mexico from 24 February 2014. To geocode this data we will search OSM to find the nearest “object” to the named settlement – in this case a “node” called Frontera (ID number 215400772)  – and link it to the unit as a base. Our Research Handbook contains the rules we use for doing this.

When we publish the data on it will be displayed in the “Sites” section of the record for 105 Batallón de Infantería along with all the sources that evidence it:

overpassblog1 sites for 105 Batallón de Infantería, Mexico

So far we have found OSM to be a good enough gazetteer. And it’s free. And it’s open licensed. And we can fix it if we need to. So you won’t find us moaning and whinging.

However, OSM has a number of issues with accuracy, coverage and change over time so we do not use OSM as a primary source of information. Instead we use it as one of a number of sources of lead information which help us piece together the geographical footprint of a security force. It’s why, for example, we don’t place 105 Batallón de Infantería directly at Venustiano Carranza International Airport, even though this is the case on OpenStreetMap. We don’t (yet) have other sources to evidence this, but OSM gives us a useful prompt to investigate this further.

I’ll cover the pros and cons of using OSM in our research in a future blog post but for now I’d like to talk about how we OSM in the early stages of research into a security force.

OSM is a useful tool for getting an impression of a security force’s physical infrastructure: lead information about where it may have bases and facilities, and the terrain that may be reserved for use by security forces  (like firing ranges,  training areas, ). How do we do this?

OpenStreetMap is a database

The points, lines and polygons (“objects”) you see on OSM are described with “tags”: for example, a tag can define a line as a “road” or a shape as a “building”, and give it a name. Incredibly, on OSM there are  over 70,000 different ways to describe an object, but the tag we’re interested is “landuse=military”.

OSM currently has 70,641 objects to which the tag “landuse=military” has been applied. OSM’s own documentation about this tag is here. The tag can be refined further by applying another tag called “military=[something]” – the [something] in question can be values like the below:

  • military=airfield
  • military=barracks
  • military=bunker
  • military=checkpoint
  • military=training_area

There are currently over 290 additional tags used on OSM to increase the specificity about the type of military land use.

How can we use this information to aid our research? The usual need we have is for a BIG LIST that we can simply go through one by one and use as starting points for searches or to cross reference data we get from other sources. Although we can view these items on OSM we can’t get such a BIG LIST. To do this we need to use a way of accessing OSM’s data called Overpass API. This is mostly by programmers but for us patient non-programmers there is a slightly easier way to use this API – it’s called  Overpass Turbo.

Using Overpass Turbo to show military land use on OSM

So, here goes. Let’s ask OSM what objects in Mexico are tagged with “landuse=military”.  Head over to Overpass Turbo:

After opening that link copy the below into the input area on the left-hand side and then hit the “Run” button (top left):

// Limit the search to “Mexico”
// Pull together the results that we want
 // Ask for the objects we want, and the tags we want
// Print out the results
out body;
out skel qt;

What’s this then? Yes, it’s a map of just those objects tagged with “landuse=military”:

Overpass Turbo – map of objects tagged “landuse=military” in Mexico (live)

Exciting! You can export this into a common geographical format (like KML or geoJSON). But I said we needed a list. Let’s alter the query a bit. Try putting this into the editor:

// Get a CSV output
[out:csv(name, "tags:name:es", "tags:name:en", ::"type", ::"id", ::"lat", ::"lon";true;",")][timeout:25];

// Limit the search to “Mexico”
// Pull together the results that we want
 // Ask for the 
// Print out the results
out body;
out skel qt;

Same data, but in a list that we throw into a spreadsheet to work more on:

Overpass Turbo – CSV list of objects tagged “landuse=military” in Mexico (live)

Even the snippet above gives us some unit and facility names to research further, as well as the locations of possible facilities that perhaps someone with local knowledge has flagged as being used for military stuff.

The queries above can be altered to search within different countries or other defined areas, examine different tags (like “amenity=police”… give it a try), and export more data (such as an object’s history).

Wrapping up

  • As well as being a map that we can search, OpenStreetMap is a database that can we query in depth.
  • Historical and contemporary military and police locations may be identified inside OpenStreetMap using the “landuse” tag. More information about the tagging system can be found on OSM’s own TagInfo service.
  • Using Overpass Turbo we can pull out that information as use it as lead information during our research. Overpass Turbo is free to use, and can output  maps and lists. The Overpass query language is documented here and there are some super examples on the OSM wiki here.

I’m sure there are more elegant ways to use Overpass Turbo than my basic code, so should anyone wish to help us out  I’m all ears (tom [at] We’re also interested in improving the data on military and police facilities that exists in OSM, … but that’s another post.

I hope this has been a helpful read, and do comment, respond and correct as needed.

Not all snapshots are created equal – a time-saving Wayback Machine technique

We’re going to write about our daily work more often.  I’ll go first with a nerdy research tip:

The Internet Archive’s Wayback Machine (the awesomeness of which I won’t bang on about) can show you when captures of the same page differ in some way from each other.

So what?

Here’s a long dead page used by La Secretaría de la Defensa Nacional (SEDENA) in Mexico to list the commanding officers of Zonas Militares (a major tier of the army in Mexico).

It exists only in the Internet Archive’s Wayback Machine now. Here are two captures of that URL – made in 2004 and 2005 respectively . The screenshots below show only the first 10 entries (of over 40 in each). Can you spot the difference?

Clipping from 8 February 2004 Wayback Machine snapshot of SEDENA army commanders page
Clipping from 3 October 2005 Wayback Machine snapshot of SEDENA army commanders page

Although the archived URL is the same, the content is not. For example, in the February 2004 snapshot SEDENA lists “Noe Antonio Ordoñez Herran” as the commander of 1/a Z.M. However, by October 2005 SEDENA lists “Germán Redondo Azuara” as the commanding officer. This is a substantive difference that we want to capture; there are also other differences between these two snapshots.

How do we approach it? First, we establish the total number of snapshots. Helpfully, the Wayback Machine tells us this for any URL that it holds snapshots for. For example, the present SEDENA page was captured 57 times:


It is likely that a page like this may have been updated regularly: the little bar chart tells us that there are differences in the sizes of the snapshots, indicating that something changed. The changes could be an update to the text in the list of commanders,  a design change of some sort that affects the page size.

Do we have to wade through all of them to find out what the differences are? No. The Wayback Machine can tell us which snapshots differ from the previous ones. Therefore, we can just go to those that differ in some way from the others and extract information from those.

To do this, we have to use another way to ask the Wayback Machine questions: the Wayback CDX server. The CDX server is a more advanced way to query the Wayback Machine, but also using your browser. It doesn’t have graphical user interface to browse the archived pages. Rather it provides metadata about the snapshots.

Here’s Wayback Machine data about our URL, but viewed from the CDX server:

Some output from the Wayback Machine CDX server.

Here’s the URL that gives you those results:

This is the few rows of the same 57 results but shown as metadata rather than as a navigable, graphical version of the web captures themselves. I’m sure you can figure how out how to turn this list into a spreadsheet that you can use to organise your research (hint: copy-paste into your favourite spreadsheet, then text-to-columns using a space as the separator).

By changing the URL a bit we can filter out snapshots that are the same as the preceding one:

We’ve tacked on two new bits to the end of the query URL:


This shows which of the snapshots have duplicates. And then:


This has the effect of removing data about snapshots that are the same as the previous one.

Overall, our results are filtered from 57 down to 31 snapshots. It’s removed 26 that were the same as the preceding one and saved us a good hour of work.

As it happens, of those 31 snapshots only 12 hold content that is useful to us. The remainder are captures of server errors, because SEDENA changed its official website (and URL structure) four times between 2004 and 2017. But that, my friends, is another blogpost.

So, to wrap up:

  • The Wayback Machine has the equivalent of an advanced query that helps us find out when snapshots of the same page differ from each other.
  • It’s called the Wayback CDX server, and you can read more about what it does on its Github page.
  • Using it at the beginning of a bit of research can save you a lot of time.

I hope this helps some of you save time when trawling the Wayback Machine, and encourages you to experiment a bit with obscurer features of well known tools. It certainly helps us create the rich data you see on




Prosecutor of the International Criminal Court receives complaint of crimes against humanity by the Mexican Army

Earlier today the Mexican Commission for the Defense and Promotion of Human Rights (CMDPDH), the International Federation for Human Rights (FIDH) and other partners submitted a complaint to the Office of the Prosecutor (OTP) of the International Criminal Court (ICC). In their report they allege that between 27 March 2008 and 16 January 2010 in the Mexican State of Chihuahua the Mexican Army committed crimes against humanity that fall under the jurisdiction of the ICC.

The complaints allege that military units operating as part of “Operation Conjunta Chihuahua” – a large and complex operation established to combat organized crime as part of the so-called “war on drugs” – committed the crimes of murder, torture, rape and sexual violence of comparable gravity, and enforced disappearances. Further, they allege that far from being isolated incidents these crimes were committed as part of a widespread and systematic attack on the civilian population, pursuant to a known and tolerated state policy, and hence should be considered crimes against humanity within the definitions of the Statute of the ICC.

Security Force Monitor is not a signatory of this complaint, but is honoured to have provided research support to CMDPDH during the course of their investigation into these events. Through a close read of existing public records we were able to provide a detailed look at the composition, chain of command and areas of operation of Operation Conjunta Chihuahua, in particular its relationship to Region Militar XI. Our full contribution to CMDPDH’s investigation is not publicly available and has been included as a confidential annex to the ICC complaint. However, on we have already published on much of the data produced about Operation Conjunta Chihuahua along with data on over 1000 units and 300 commanding officers of the security forces of Mexico going back a decade.

Now the complaint has been submitted the Office of the Prosecutor is obliged to assess it and move to start an investigation if it finds there is a reasonable basis to do so. We hope that the Prosecutor is persuaded by the complaint and we will track developments here on the blog. The full communication is available in Spanish from CMDPDH and English from FIDH.

En español:

Fiscal de la Corte Penal Internacional recibe denuncia de crímenes de lesa humanidad cometidos por el Ejército Mexicano

El día de hoy la Comisión Mexicana de Defensa y Promoción de los Derechos Humanos (CMDPDH), la Federación Internacional de los Derechos Humanos (FIDH) junto con otras organizaciones colaboradoras, presentaron una denuncia ante la Oficina del Fiscal (OTP) de la Corte Penal Internacional (CPI).  Dentro de su informe, se denuncia que entre el 27 de marzo de 2008 y el 16 de enero de 2010 en el estado mexicano de Chihuahua, el Ejército Mexicano cometió crímenes de lesa humanidad que caen bajo la jurisdicción de la CPI.

Las denuncias alegan que las unidades militares que operaban dentro de la denominada “Operación Conjunta Chihuahua” (una compleja operación militar establecida para combatir el crimen organizado como parte de la llamada “guerra contra el narcotráfico) cometieron los crímenes de asesinato, tortura, violación y abuso sexual y desapariciones forzadas. Además, alegan que, lejos de ser incidentes aislados, estos crímenes fueron cometidos como parte de un ataque generalizado y sistemático contra la población civil, de conformidad con una política estatal conocida y tolerada por el Estado, y por lo tanto deben considerarse como crímenes de lesa humanidad dentro de las definiciones del Estatuto de la CPI.

A pesar de no ser un signatario de dicha denuncia, el Security Force Monitor tiene el honor de haberle brindado análisis de apoyo a la CMDPDH durante el transcurso de su investigación. A través de un análisis minucioso de los registros públicos existentes, pudimos proporcionar una visión detallada de la composición, cadena de mando y áreas de operación de la Operación Conjunta Chihuahua y en particular su relación con la Región Militar XI. Nuestra contribución total a la investigación de CMDPDH no se encuentra disponible públicamente y se ha incluido como un anexo confidencial a la queja presentada ante la CPI. Sin embargo, en ya hemos publicado muchos de los datos producidos sobre la Operación Conjunta Chihuahua junto con datos de más de 1000 unidades y 300 comandantes de las fuerzas de seguridad de México que datan de hace una década.

Ahora que la denuncia ha sido entregada, la Oficina del Fiscal se encuentra obligada a evaluarla y proceder a iniciar una investigación si considera bases razonables para hacerlo. Esperamos que el Fiscal sea persuadido por la queja y tome las medidas adecuadas, a las cuales daremos seguimiento en el presente blog. La comunicación completa se encuentra disponible en Español en el portal de la CMDPDH y en Inglés en la página de la FIDH.

Analysis of sources for data on security forces: can computers help us out?

Image: Security Force Monitor uses sources like news articles, NGO reports and official press releases to create data on the structure, leadership and behaviour of security forces

Can computers help Security Force Monitor’s researchers increase their speed and accuracy when extracting relevant data about security forces from the text of news articles and reports?

Over the last few months Yue “Ulysses” Chang, a masters student at the Data Science Institute at Columbia University, has interned with us to help us explore this question. The quick answer is “yes, 79% of the time.” The longer (and hopefully nice and readable answer) starts in this blog post, the first of a short series.

How do we create data about security forces?

Each week at Security Force Monitor we identify and read 100s of news articles, reports, maps and datasets – these are the “sources” out of which we pull thousands of little details about organizations, names and locations. We stitch these together to create the rich view of security force structures and commanders that you can search through on

This is time-consuming work. In most cases after we’ve found a useful source we just have to read through it, identify the snippets of information we need and then copy, paste or re-type them into our databases. Here’s a few paragraphs from a typical source – “Police IG Redeploys AIGs, CPs For April 11 Polls” – published by Channels TV on 10 April 2015:

Image: excerpt from “Police IG Redeploys AIGs, CPs For April 11 Polls”, Channels TV (Nigeria), 10 April 2015.

We can use the information in this source to support the below statements, and enter the relevant values into a database:

  • On 10 April 2015 (the publication date of the source) Inspector General of Police, Assistant Inspector General, and Deputy Inspector-General of Police are ranks in the police force in Nigeria.
  • On 10 April 2015 Force Public Relations Officer is a title in the police force in Nigeria.
  • On 10 April 2015  Suleiman Abba is a person holding the rank of Inspector General of Police (IGP) in Nigeria.
  • On 10 April 2015 Aigusman Gwary is a person holding the rank of Assistant Inspector General of Police (AIG) in Nigeria.
  • On 10 April 2015 six Deputy Inspectors of Police “coordinate activities” in six geo-political zones.

Every one of these data points has at least a single source. For example, to make our data on units – distinct parts of security forces such as army battalions or police divisions – we looked at around 3500 unique sources taken from over 200 different publications.  From these sources we were able to evidence 25,505 data points with a single source, 2086 data points with more than 10 distinct sources, and 59 data points with over 40 distinct sources.

Table: How many data points about the units on are evidenced by more than one unique source?

We presently cover branches of the security forces of Nigeria, Mexico and Egypt.  As we expand our coverage to other countries we will need to consider ways of reducing the time spent and risk of error inherent in this part of our research process. If we can reduce the time we spend searching, cutting and pasting bits of text, then we can spend more time cross-referencing and producing interesting analysis from the data. Could we get more help from computers than we currently do?

“NLP”, “NER” … ?

Computers can read too. Sort of.

Natural Language Processing (NLP) is a long-established field of computer science that looks at how machines relate to people’s speech and writing, and ultimately how they can comprehend information passed to it by a person. The fruits of NLP research provide technologies that power everything from the recommendations you get on search engines, those (irritating) automated voice call systems, and the (less irritating) digital voice assistants. Named Entity Recognition (NER) is the sub-field of NLP that gives computers the capability to pick out things that people can recognise in text – like names, persons, organizations, locations, dates. Could they be applied to our work?

We can start exploring this question very quickly by using one of numerous “off the shelf” NLP and NER toolsets. To test our ideas out we have chosen a toolkit called spaCy.  This has the benefit of having a wide range of functions, and being free and open source – this enables us to use the toolset without direct cost.

Without any modification spaCy can assess text and identify persons, organizations, locations, dates and lots of other types of entities. It can also be trained to improve its ability to detect the above entities (like adding in new geographical model), and identify new entities such as rank and role, or connections between entities. What’s not to love?

NER and real sources

Let’s give it a try. We can take the  text from the sample news article we analysed above, and place it into into spaCy.  It will highlight different parts of the text that it considers to be entities:

Image: Use of unmodified, untrained spaCy NER algorithm to identify people, places and organizations in text (see an interactive version of this example).

The performance here is is ok, but it is not without problems. For example, spaCy correctly picked out all but one of people mentioned in the article (“Aigusman Gwary”, who it tagged as an Organization rather than a Person). It has also successfully identified Lagos and Bauchi as geo-political entities (“GPE”) but misses “Akwa Ibom” and “Rivers”, and mis-categorizes “Jigawa” as an organization. There are other misses in there too.

Bringing this into the work of Security Force Monitor

In this post we’ve outlined the challenges we face, and in broad terms the way that we see this set of technologies offering an opportunity to address them. The intriguing question for us is how to take the raw capabilities of NER and have them benefit our research work in specific and effective ways. We have a long list of things on our mind, including:

  • The skills and financial costs that will be required to develop, implement and maintain such a system in a way that is reliable and effective.
  • Whether we can improve the performance of the algorithm by using the data we have already collected to train spaCy to better pick out what we are looking for in a source.
  • How to reconcile the stream of information coming in from NER with the data we already have – for example, what process will we use to figure out if “Jane S Smith” and “Jayne S Smith” are the same person?
  • How we evaluate NLP and NER systems so we know whether they are getting better (or worse!).
  • The type of workflow and user interface that would be needed to bring these capabilities effectively into our research work so they are actually helpful.

In the next post in this series, Ulysses and I will start digging into these questions and revealing some of the work that we have done so far.

(Article edited on 22 February 2018 to correct typos and clarify wording)

February data update on – SARS Nigeria, Mexico military garrisons, new Egypt units

Since December 2017 we have made published two updates to, adding a large number of new records, expanding others and making some corrections. Cumulatively, these updates increase the data available on by 25%. In this blog post we’ll look in depth a recent restructure of the Special Anti-Robbery Squads (SARS) of the Nigeria Police Force and give a brief overview of other updates.

Special Anti-Robbery Squads (SARS) – Nigeria Police Force

Changes in the chain of command of Special Anti-Robbery Squads (SARS) of the Nigeria Police Force

SARS are a specialised type of unit of the Nigeria Police Force. They were established in each state and the Federal Capital Territory (FCT) to combat violent crime. Civil society groups have reported on allegations of human rights abuses by SARS for at least 15 years. In its September 2016 report “You Have Signed Your Death Warrant” Amnesty International documented numerous allegations against SARS across Nigeria, including acts of torture and other cruel, inhuman or degrading treatment or punishment. We have carefully extracted these incidents from Amnesty’s report and made them searchable on

In December 2017 Nigerian citizens rallied around the #EndSARS hashtag on social media, using it to make allegations and share experiences of violence and corruption by SARS personnel. #EndSARS culminated in a number of protests during which the movement’s leadership demanded the squads be disbanded. In response, the Inspector-General of Police did not disband SARS but restructured the units… twice. What, if anything, changed?

For a long stretch between 2010 and 3 December 2017 the SARS in each state and the FCT of Nigeria had two different and simultaneous chains of command. Each state/FCT SARS was under the Criminal Investigation Division (CID) for their state/FCT while also being “coordinated” by a Commissioner of Police for SARS who was under the Federal Criminal Investigation Department/”D” Department of the Nigeria Police Force. Ultimately both chains of command end at the Inspector General of Police (IGP) at Force Headquarters.

On 4 December 2017 the IGP announced a dramatic reshuffle: SARS in each state/FCT would report to the Federal SARS, which itself would be moved under the “B” Department/Operations Department at Force Headquarters in Abuja. Thus for a brief moment all of the SARS units in each state had a single chain of command.

It may be that this was a mistake because just over a fortnight later on 22 December 2017 the IGP made another announcement: SARS would return to having two simultaneous chains of command. SARS in each state/FCT would be under the command of the state/FCT Commissioner of Police (through the CP’s deputies in charge of operations) as well as continuing to report to the CP in charge of Federal SARS who was still under the “B” Department/Operations.

So, the overall effect on the SARS chain of command is the removal of State CID, along with a shift in reporting from “D” Department (Investigations) to the “B” Department (Operations) at Force Headquarters. The impact of these restructurings on SARS themselves are difficult to assess. A past reorganization announced by the IGP in November 2015 – which split SARS in each state into “operations” and “investigations” branches – apparently was never actually implemented on the ground. Amnesty International reported SARS officers they interviewed in June 2016 were “unaware of the IGP’s announcement [in November 2015] that SARS ha[d] been split into two units for operations purposes.” For now, SARS is also still listed as under the “D” Department on the Nigeria Police Force’s website. We will continue to watch developments closely, update and extend our data on SARS as more information becomes available.

You can view the updated data on the Special Anti-Robbery Squads (SARS) of the Nigerian Police on

Other updates to data on security forces in Nigeria, Mexico and Egypt


As well as our close look at SARS above we have updated with data on police units in Delta and Bauchi States in Nigeria. Further, we have now added allegations of human rights abuses by security forces against pro-Biafra protesters in the south-eastern states of Nigeria. In its November 2016 report “Bullets Were Raining Everywhere” Amnesty International reports numerous allegations of extrajudicial killing, torture and arbitrary arrest and detention committed by security forces against pro-Biafran protesters between August 2015 and August 2016 in Nigeria’s Anambra, Abia and Rivers States.

View the updated Nigeria data on


We have extended the Mexico dataset on to cover Military Garrisons (“guarniciones militares”) and their commanders. Garrisons can play an active role in military operations and often command smaller units as well. One example of this is Guarnición Militar de Ciudad Juárez which participated in a major military joint operation Operación Conjunta Chihuahua and commanded both the 9 and 20 Regimientos de Caballería Motorizado (motorized cavalry regiments).

In an earlier upload of data we had omitted full descriptions of a number alleged human rights abuses in Mexico. We have now corrected this.

View the updated Mexico data on


For our data on Egypt, we have added initial data on top level military structures and entries for police units in Aswan and Al Sharqia governorates in Egypt. We’ve also included a small number of allegations of human rights abuses by police in Egypt as reported by Human Rights Watch (HRW) in September 2017.

View the updated Egypt data on


Launching – a power tool for investigating security forces

It’s a big day here at  Security Force Monitor. We’re excited to reveal our first official product:

WhoWasInCommand shows the composition of security forces, their commanders, and the locations of operations and bases makes it fast and easy to find detailed information about the chain of command, areas of operation, commanders and bases of the police, military and other security forces of a country and discover links to alleged human rights violations.

This platform is a unique resource containing a level of detailed data about security forces that has never existed before. It’s the result of an enormous amount of work – and would not have been possible without extensive advice and help from civil society partners. We hope that you find this new tool useful.

10 reasons to use

We’d like to point out some of the things that we think make a powerful and effective research tool:

  1. Unique, high grade research: contains thousands of units and commanders from the security forces of Egypt, Mexico and Nigeria going back over 10 years. We are committed to expanding our coverage for those and other countries. Expect more data soon!
  2. Start with search; find things fast: It’s easy to find what you want, no need to navigate unnecessarily.
  3. Refine your search with powerful filters: Your search results can be refined using nearly 30 different dimensions about location, time, organizational attributes and relationships, and biographical details of personnel.
  4. Crystal clear views of the data: We’ve designed simple maps, tables and tree charts to present the data we have in the clearest ways possible.
  5. Check out where every bit of data comes from: You can take a look and get at the sources used to evidence every single datapoint on Also the methods we have used to create the data are fully documented in our Research Handbook.
  6. Take your findings home with you: Search results, along with any dossier on can be downloaded into a spreadsheet along with all their sources.
  7. Get help when you need it: contains help and tips throughout, explaining how different bits of the site work, and what the data presented means.
  8. Use it in your language: is currently translated into English and Spanish, with several more languages to come.
  9. Use it on mobiles and tablets! is mobile friendly.
  10. Get your own WhoWasInCommand: The software powering is open source, which means you can set up and run your own copy of the platform.

Security Force Monitor has partnered with DataMade to create DataMade has operationalized and refined Security Force Monitor’s data structure, created a powerful open source platform to put the data online, and made a significant contribution to the concept and design of

We hope that aids the work of journalists, human rights researchers, advocates, litigators and others working to make security forces accountable to the public they serve.

We’re keen to hear what you think about Email us at

Learning from our users – first feedback on our prototype

In mid-April we publicly released the first version of our web application for feedback. We sought out advice from human rights researchers, international criminal litigators, investigative journalists, and policy advocates – spending over an hour with more than 45 users. Our whole team was struck by the willingness of our colleagues to dedicate time to giving us feedback. To everyone that took the time to talk with us – thank you!

This post will cover what we’ve learned from our users, how their feedback helps our mission, and why we’ll be reaching out again shortly.

Interviewees really liked… Interviewees had questions about …
  • The content itself
  • Charts showing command structures
  • Being able to access all the sources
  • Ways of going forward and backwards in time
  • Background information about security forces in a country
  • Showing analysis rather than just data
  • Visual clutter and data density
  • How to find data quickly
  • Downloading our information
  • Not knowing how data was selected for inclusion
  • Completeness of the data
  • Slow application responsiveness

We are answering the right questions, but not always in the right way

Users strongly validated the premise of our work – they very much wanted (and generally found it hard to find) information on the organizational structure, command personnel, location and areas of operation of security forces tracked through time. Interviewees could see the value of the dataset – in part or as a whole – to their own work. They were also intrigued by the visuals on offer, liking our ambition.

Our visualization of the command chart was a huge success, users loved being able to see that information in a visual way. Users had extremely positive views about actual data. They got glimpses its value, in particular where we included the analysis we can produce using the data. For example, for each alleged human rights incident we included a list of nearby units, which numerous interviewees found clarified the overall purpose of our research. They also liked the ability to see sources for each datapoint, which would help them appraise the data for inclusion in their own work. While users did not find the timeline functionality intuitive to use, when we explained how they could “time travel” through our data to see the command tree, commanders and other data at a particular point in time they were thrilled.

The biggest issue for users was that they were often overwhelmed with information, particularly in the initial map view of country. This made it difficult to grasp what they were looking at, or where to go next. There were many questions related to our terminology (what do we mean by area of operations, affiliated person, etc). Users often had difficulty navigating around the application from page to page, and our tools to sort through the visuals (like the filters on the map page) were not intuitive.

Users Want More of This Users Want Less of This
moreofthis lessofthis

What we will do next

The 45 people we interviewed told us straight what they liked and did not about our work so far They have given us good guidance on the direction we need to take.

  • Make it far, far simpler to use – less of a stand-alone application and more of a webpage
  • Simplify the presentation of key information, using visualization more sparingly, offering contextual guidance where needed.
  • Make it ‘search first’, giving lots of ways of find, sort and filter throughout the application.
  • Speed it up, and give lots of cues about how user things change things when the user does something.
  • Let people take the data home.

We have taken the feedback users have given us and will launching a radically new and improved application in the coming months – so stay tuned for more updates!

Why I started the Security Force Monitor

by Tony Wilson.

Today, I’m sharing something with you that I’m proud of.  It’s a new research tool we’ve created – an early version that shows our research, and embodies our reason for being.  It brings to light something we have never seen so clearly in one place before: the structure and operations of security forces, surfaced and arranged from thousands of publicly-available sources.

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You can visit our prototype online here. Currently it covers security forces in Mexico and Nigeria – over 1,000 discrete organizations and nearly 900 affiliated persons.

How could all this information not already exist?

I started the Security Force Monitor in order to address a simple problem – the lack of detailed information about the police, military and other security forces of a country. At the time, I was trying to help advocates raise human rights concerns about U.S. security assistance to Bahrain. Since the protests that began in February 2011, rampant human rights abuses had been documented, but it was incredibly difficult for human rights researchers to identify specific perpetrators because the security forces were not transparent. This was extremely frustrating since it made advocacy supporting human rights conditionality on security assistance even harder. So, the task was clear: find detailed information about the security forces of Bahrain.

After several weeks of pilot research together with a colleague we had found data from hundreds of sources, and compiled them into an ever-lengthening Word document. This initial research demonstrated the information gap could be filled, but just as quickly a second problem arose – making sense of large amounts of quite detailed data. Using the limited skills I had, I created a rough Google Map and a rudimentary organizational chart, in an effort to make sense of all that data.

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Even with these basic tools, I could see compelling connections between alleged human rights abuses and specific units and commanders. But the limitations were also evident. It was clear to me that in order to be a sustained effort the Security Force Monitor could not just work off of a text document or even a spreadsheet. It would need professionally-developed tools that a team could use to make accurate data easy to create, and a way of publishing it that would aid others in their own investigations.

Armed with some sketches of what a potential platform could look like, I interviewed almost 90 journalists, advocates, human rights researchers and others engaged in public interest efforts and asked them what would be useful to them and their work.

A capture from initial sketches for an application, showing a command tree, and a time slider

Their feedback guided the genesis of Security Force Monitor.  I have been fortunate to gain the support of the Open Society Foundations and the Oak Foundation, win the Knight News Challenge on Data, and pull together a great team – Tom Longley and Michel E. Manzur. The Security Force Monitor also found a welcoming institutional home at Columbia Law School Human Rights Institute and has begun to build an exceptional Advisory Council for the project. To move from concept to tool we have worked with the creative civic technologists at DataMade, FFunction and OpenNorth. Together we worked to create the datasets and produce our first attempt at a product, the prototype that we are releasing today.

The picture it shows of security forces is rich and detailed, and changes with time. The prototype platform shows the changes that occurred over time as units were created, moved or disbanded; and as commanders were promoted, retired or fired. Finally, all our work is transparent: every data point is sourced with citations back to where we got our data.

This prototype is a big first step. When we were getting our idea off the ground we talked with almost 90 journalists, activists, researchers and policy experts. Now, with this prototype in hand, we will be conducting even more interviews over the next several weeks on what does and does not work in order to develop an even better version.

You may be getting an email from us very soon. In fact, if you have thoughts and feedback, email me directly – tony [at]

Our mission is simple but bold. The Security Force Monitor will organize every piece of public information about security forces. We’ll produce research of the highest quality that will help make security forces more transparent. I believe that producing this research will aid journalists, civil society, human rights researchers, oversight efforts and others in making security forces more accountable.

You can help us do this. Take the first step by using our prototype and telling us what you think. Working together we can make the Security Force Monitor an indispensable digital service for transparency, accountability and human rights.