It goes without saying that there have been quite commendable efforts towards initiating the development of a Spatial Data Infrastructure in Kenya at w/e level of the said implementation effort; the efforts could be towards a narrow implementation just within an organization or broadly at county or national level like the case for KNSDI and the recent spree of efforts at county levels. The SPatial Planning Act for CGs that was tipped to accelarate the Geospatial Capacity of the CGs failed to see light of day a/w.
It also a general consensus among stakeholders that non of these efforts have tangible or any documented results – simply “white-elephants”. Or at the very least I can say that any results if there was, has escaped my attention till this moment. However, the goal of this article is not to examine efforts made,but to enumerate the key principles that will bring us a little more closer to realizing this continually elusive pillar of the Geospatial industry in this country.
The first two of the five are policy, institutional framework related
Developing visions and Agency missions that we believe in
Statement of desirable positions in the administration, delivery of public services and sustainable development; that are then at lower levels of governance like the counties converted into operational goals. In the areas of spatial data management, vision of its acquisition, cataloging and sharing and discovery is a necessity which then implementer can define the specifications that will deliver the visions. I believe we still luck the vision and the purpose to go past the SDI debate and actualize the talk.
Principle of subsidiarity and proportionality
At any one time decision making is going to be the responsibility bestowed upon one agency or a particular level of governance (centralized). Undebatable. However, key issues in SDI that we have to deal with are, involvement of multi-governance levels, multi-sectoral, citizen users and professionals. To implement an SDI just like any other system that sees this dynamics, some level of participation and inclusion of the stakeholders and consumers is key. “Allow decentralized participation as much as possible”, “centralize decision-making at the top if necessary”. Then also, consider the diverse knowledge in your stakeholders, professionals, and users just so they are not sidelined.
Then on the technical sidelines…
This has been a debate for a long time, and some of us have gotten religious about given data formats. The key thing here is to achieve Interoperability, minimize the impact on stakeholders, providers, and consumers adoption of the technical implementation by offering them the ability to integrate spatial and non-spatial data and services from across the various levels of implementations, which could be agencies, CGs and national level. Take a case of the INSPIRE Initiative for European countries which has specified standards dataset formats in which data can be represented and shared. And this has been provided as a directive under 34 data themes for the European countries. We have to think about this, formats, models, transformation. And BTW beside just data formats we still have the elephant in the room (Black though- it’s real), Coordinate Reference Systems. I can tell you this is in bad state, currently even data that its accuracy is sensitive as cadastral data exist in as many CRS as our imagination.
Infrastructure and Software
Well, maybe we are not as religious about data formats as we are about software classes. I always try to slide past this debate, but today I have to get right into it, this is fundamental decision point. Commercial (well supported products) versus Open Source products (which also have commercial support anyway). How much investment are we ready to make on Infrastructure versus Software, versus Policy/Framework development versus Capacity Development. Funds are as limited as they come, but all these four are priorities to see your SDI implementation through. It is key to map out out an effective roadmap that will ensure all are provisioned adequately.
However, that was a digression, what is important here is Scalability, once again Interoperability (seems popular around here), and the usual stuff usability, maturity level blah blah. SDI architecture is changing fast with the recent advancements in Geospatial technologies and the introduction of new flavours of the day in technologies and concepts of managing Big Data. Be careful of the flavours of the day tech stuff. Balloon you with excitement then bust right in you face. Data growth is exponential today and so are the needs of consumers and stakeholders to work with it.
Key question to accelerate the success of your SDI is, are you provisioning the right infrastructure and deploying the right solutions backed with robust institutional frameworks, oh, and do not forget the guys behind the computers, are they technically equipped and fluid enough to manage growth of your consumers and the demand of your entitled stakeholders. And this is gotta be a strategic plan for 5 years down the line without necessitating replacement of the whole platform. Read the rationale for this here on Andrew Dearing’s interview w/ Geohipster on Developing Geoserver for Scalability.
I can’t stress this more, pay attention to “technical interoperability”, this has been enabled by open standards and has seen mega changes in workflows and made institutional coordination and data sharing easier. If the national government ran KNSDI platform on Software choice A and infrastructure choice X, would your shared stakeholders and consumers combine the data and services they get from there with those from your Software choice B and infrastructure choice Y?
Implementing an SDI is no small talk, human resource and funds go a long way in delivering a successful SDI. In a two-level governance scenario like Kenya, decentralization of the responsibilities, and expectations should be coupled with the decentralization of human resource and financial capacity to deliver on the mandates.
Data curation which involve format transformation is an expensive endeavor, and hungry for skills. To pull it off, you need to invest in Capacity development.
In the debate of SDI Implementation, I believe as a community we need to revisit the classical definition of a Spatial Data Infrastructure and place it in the context of our country especially now considering the two level of government and numerous mapping agencies. Focus on Framework of policies development, strategic partnerships, before delving into technical realms of SDI. This is where the battle is won. It is where “will” is necessary, the rest require technical and financial capacity which is in plenty.
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