Why Open Data is More The Mindset Than The Technology…
February 6, 2014 — 6:54

Author: linet  Category: #DataScience  Comments: 0

opendata
During my time at the Kenya Open Data Initiative, we worked very hard to make sure that we had the right technologies in place to steer the initiative. From the very nice Socrata platform to great partnerships with many private sector players and development partners.

Over the past couple of weeks, I have seen discussions around how liberating data would be so great for developers and all the technical peoples. Well, this is something I have known for over 2 years now. The importance, the pros and cons. Not very much seems to change.

What caught my eyes though, what this post is about, is the forgotten group of non technical data holders. Beyond having the best technologies, Open Data is very much a mindset problem than it is the platforms on which we put this data on. The people who refuse to give data have deep organizational and cultural backgrounds of a NO DATA SHARING policy. The silo mentality in government institutions, the competitive edge mentality in private sector, the private data mentality among individuals. All these contribute to a group of people who do not care about technology but who are actually more afraid of their data being shared especially on platforms they do not control.

I met one data provider this week, who could not get information from a colleague in the same ministry but from different departments as “they had not received an order from above.” This is so frustrating and this is what we need to change.

I was fortunate enough to sit in the committee that was curating Kenya’s Access to information bill and at the time, while I was pushing that there should be a clause requiring all government institutions to make their data public and in open standard recognized formats, someone reminded me that that would take way longer to happen as most government infrastructure could not allow the people to do their work and at the same time work on making the data available in those open formats. Now I appreciate what that wisdom meant.

So even as we all crowd in making technological platforms to solve open data issues, let us take a minute to remember that open data is more about releasing data, having the pro-active drive to making data openly and publicly available whether on books, PDF, files, csv etc. Let us start with what we can get.

And, as we campaign for data solutions, let us remember to campaign for data to be released without putting in place stringent measures on the data holders. I guess the data business is the only place where you don’t create supply of new versions by killing demand of older versions. We need to demand the books and PDFs, to create the supply for open formats like CSV and xls.

World Bank Finances Disbursements per Country
February 3, 2014 — 12:28

Author: linet  Category: #DataScience  Comments: 3

Have you ever wondered where the World Bank disburses money to? Well, wonder no more!

The map below shows where the Bank gives money to and how much.

Yes, yes, I would have also loved to know which of it is loans, grants etc but this is what we have for now so, enjoy!!

 


View Larger Map

Turkana County, Did You Know..
January 30, 2014 — 19:45

Author: linet  Category: #DataScience  Comments: 0

Turkana county – Governor Josphat Nanok Koli, has always been in the news for all the wrong reasons. Poverty, death, hunger etc and although there is this “glimpse” of hope on the discovery of water and oil reserves, this has been more of better news for the country’s image than it has to the people of Turkana who continue to face harsh times that are leaving them helpless and unable to compete at national level.

This project aims to bring to light some facts you probably did not know about Turkana county. The main source is the government statistics from the Kenya Open Data Initiative.

The Turkana age groups pyramid according to the National Census 2009
They account for 855,399, or 2.5% of the Kenyan population (with more males than females)

Powered by Socrata

According to KIHBS, in 2005/2006, the population of Turkana county was 538,949 and the number of poor people was 500,662 which is about 93% of the population. Now, while 2006 is a very long time, looking at the other statistical indicators, its easy to see that not much of that has changed for the better.

With about 148 health facilities in Turkana county (as per the Master Facility List of MOH),Turkana has the following distribution of medical practitioners according to the county distribution of medical practitioners report:

MedicalPractitionersTurkana

Turkana county also has the largest population of donkeys in Kenya (census 2009) at 558,187 donkeys (about 1 donkey for every 2 people)

Powered by Socrata

Turkana is not only rich in oil and water but was also rich in cattle as shown on the cattle distribution map below. Click on any of the circles to see the location and livestock population distribution. Note the light green large circles:

Powered by Socrata

Below is the Turkana County budget for the financial year 2013/14 via The Commission for Revenue allocation.
TurkanaBudget

Turkana county collected KES 26,779,267 in the first quarter of financial year 2013/2014 vs its target amount 351,838,651 via Controller of budgets.
Collected vs target

Despite its challenges, Turkana county is performing averagely when it comes to access to education, especially at primary and pre-primary levels as shown below.

Food Security, your meal per kg cost of ingredients
January 29, 2014 — 11:51

Author: linet  Category: #DataScience  Comments: 0

20140128_122213(0) During this time when our fellow citizens in Turkana County are struggling with hunger, there have been more questions asked than answered, especially when the county intends to spend 450,000,000 on the governor’s office (Story for another day) while people go for days without food.

There is a lot of talk about food security and all but today I wanna share a small analysis of how much a meal would cost you at wholesale price, if you were to consume this meal at KG quantities of all the ingredients that are used to make that meal. Please note that these are typical Kenyan meals, and some ingredients like oil and water have been excluded and the assumption is that these meals are ready hence no need for cooking heat.

The table below is a representation of the various ingredient costs per Kg (as literal divisions of wholesale prices at larger quantities (so this could be a little higher.))

Target County Revenue Collection by Q4 vs. Collected at Q1
January 27, 2014 — 11:39

Author: linet  Category: #DataScience  Comments: 0

Revenue absorption rates for county governments.
January 24, 2014 — 9:18

Author: linet  Category: #DataScience  Comments: 0

Revenue absorption rates for county governments. | Infographics

Top 10 counties 2012/2013 Quarter 4

Bottom 10 counties Quarter 1 financial year 2013/2014

Bottom 10 counties Quarter 4 financial year 2012/2013

Bottom 10 counties Q1 financial year 2013/2014

County Governments Revenue Trends 2012/13/14
January 23, 2014 — 15:38

Author: john  Category: #DataScience  Comments: 2

A few weeks ago, the controller of budget’s office released the budget reports for the first quarter 2013/2014 for the counties. Also in the data sets, is the data from the final quarter of the financial year 2012/2013 (March – June), a few months after the elections.

Most counties collected revenue which are very low compared to the annual revenue targets from the
local sources. The Counties projected to collect about Ksh 67.4 billion in 2013/14 but were able to only
raise Ksh 4.4 billion which is just 6.5% of the target figure for the year. In an ideal scenario the Counties should be able to collect able 25% per quarter.

There are a number of reasons for this huge gap, first most counties set very ambitious figures
compared to what the local authorities used to collect previously. The other reason mentioned by
the controller of Budget was the fact that most counties took long to pass their Finance Bills which
authorizes them to collect revenue.

However, one thing that requires further probing is the fact that the Total revenue collected by all
counties has been dropping month after month for the seven months between March and September
2013.

These graphs compare the  counties on three key aspects but the most interesting (click on the button to see results) is the % of revenue target achieved vs the annual revenue target for a particular county. The annualized figure (*4) shows you how much the counties would make if the figures remained constant at the end of the financial year.

While some counties look like they will reach their target revenue collection, some of the counties were too ambitious hence counties like Kakamega were only able to achieve 1.1 % of their target revenue collection.

Let the graphs do the talking, click away :)

The following infographic shows the total revenue collected by all the counties starting the closing quarter of financial year 2012/13 and starting quarter of financial year 2013/14. It is clear to see that there has been a great decline in revenues collected which means that the target revenue collection will not be reached if the trend continues.

Total revenue collected March – September. | Infographics
What Our National Anthem Says About Us, Kenyans.
January 23, 2014 — 7:57

Author: linet  Category: #DataScience  Comments: 0

“People often say that motivation doesn’t last. Well, neither does bathing – that’s why we recommend it daily.” – Zig Ziglar

Kenyan

Over the last week, I have taken some time to analyze the Kenya National Anthem that was written to celebrate our independence in 1963 and there after, like many other countries showing what we stand for.

The national Anthem is sang at every official event, Mondays and Fridays in schools, during movie showings etc. It is the first song I Jam to when playing my saxophone.

We are creatures of habit, we are what we repeatedly do, say, eat etc.So let us analyze the national anthem and what it says about us:

First Stanza:

O God of all creation
Bless this our land and nation
Justice be our shield and defender
May we dwell in unity
Peace and liberty
Plenty be found within our borders.

The first stanza, which is what most Kenyans know by heart is a prayer. Well, the entire anthem is a prayer but this particular stanza calls upon God. Its an ask, it says little about us doing but a lot more about ‘expecting.’ It is a little crazy that I even want to relate this stanza to the mushrooming of churches and religion in Kenya? Our liking for always calling on the government and in very few cases exercising our ability to create solutions?.. Then next two stanzas will explain this analogy.

Second Stanza:

Let one and all arise
With hearts both strong and true
Service be our earnest endeavor
And our homeland of Kenya
Heritage of splendor
Firm may we stand to defend

The second stanza calls on us, Kenyans to arise, to be brave and to defend our nation. It calls on us to go into action. It calls on us, it is not us calling on 3rd party help, its calling us! Now how many people do you know that remember this stanza? How many events have you attended that have gone as far as the second stanza? Could this be the reason why a lot of Kenyans are so lag when it comes to security issues? When asked to report any suspicious behavior, when it comes to protecting our fellow citizens.

Third Stanza:

Let all with one accord
In common bond united
Build this our nation together
And the glory of Kenya
The fruit of our labor
Fill every heart with thanksgiving.

By now, if one does not know the second stanza, they have probably never heard of the third stanza. If you were at an event that the second stanza was not played, chances are, they never played the third stanza.

This reminds me that even at presidential functions, the national anthem is played on instrumental and not very many people go beyond the 1st.

Let us all together, unite and build our nation, together, so that all our efforts may be rewarding to us. This is my one liner translation of this stanza. On the contrary, Kenya is quickly becoming an, ‘every one for themselves and God for us all’ kind of nation. We have seen the greed with which those in office have looted our country, the impatience with which the motorists rock the roads and break the rules. Every single day i come in contact with people, I have seen how everyone wants to do what fits them there and then but rarely for the common good.

This Anthem is asking us to, in addition to Seeking God, defend together. Brave together. Work together. Pull together. Build together. With one accord, for this our homeland. Does this reflect anything about you? So as you read this post, dear Kenyan, ask yourself, in your actions, does any knowledge (or lack of it) of the National Anthem directly affect your behavior?

MDG 4: reduce child mortality
January 15, 2014 — 6:58

Author: linet  Category: #DataScience  Comments: 0

Millennium Development under five milestones, Kenya, Uganda, Tanzania, Rwanda.

MDG 4: Reduce by two-thirds, between 1990 and 2015, the under-five mortality rate.

Compared to her neighbours, Kenya’s annual rate of reduction Under five deaths in children is much slower.
Over the years, the death of children, under the age of five has reduced three times faster than Kenya’s, while that of Tanzania’s is four times faster than Kenya.

All these countries had high numbers of under five deaths in 1990 compared to Kenya, when the journey to the millennium development goals Began.
Ethiopia has already met its mortality rate target of 68, while Tanzania, Rwanda and Uganda are closing in on the gap.

According Child Mortality Report 2013, Kenya is likely to miss the targets of all the MDGs as policies due to laxity in implementation.

How countries are feted in reducing number of under-five deaths per 100,000 live births.

Post by: Trudy Mbaluku

The relationship between education and poverty.
January 8, 2014 — 11:00

Author: linet  Category: #DataScience  Comments: 2

An Interesting trend that i have noticed in the inequalities data, that is really a no-brainer to anyone looking at the data is that one that draws the direct relation between poverty and access to education.

Below, I examine the richest and poorest counties and the ones with the most and least access to education beyond secondary school level..

My conclusion in this case then becomes that to fight poverty, what we need is education, educate the people, help them help themselves!