Data needs versus available data
What is evident from this survey is that the data needs across the indicated thematic areas are greater than what is available. This implies that institutions (for various reasons) are unable to generate all the required datasets. For instance, most institutions surveyed indicated that the data items they required most for their work were those in the land use data class. In fact only the NLC manages the entire array of data items as listed in the land use data class. This is to be expected as it is within the mandate of the NLC to do so. But it further emphasises the need for a network where - through exchange and leveraging of comparative advantage - most data needs can be satisfied.
Data collection methods Limitation of datasets held by institutions
Data is obtained from routine and non-routine data collection methods. Examples of routine data col- lection include geographical data on physiography/ topography, roads, water distribution, water quality and water production collected by RECO-RWASCO using Global Positioning Systems (GPS). It is col- lected at local, provisional and national level at variable scales. Other institutions which carry out routine data collection include the Ministry of Inter- nal Security (MININTER) that records daily events which are then reported to headquarters. Others are MINISANTE which amongst other things records births daily.
Non routine data collection methods include sur- veys, population census and quantitative or qualita- tive rapid assessment methods, among others. These include the twice-yearly crop assessment surveys and the 5-year agricultural surveys that are carried out by MINAGRI; the 5-yearly Service Provision Assess- ments and the Census of Population and Housing conducted every 10 years by NISR.
24
Age of dataset Completeness Quality/accuracy Scale
Resolution Absence of data 020406080100% Figure 3: Limitation of datasets held by institutions
Some of the above problems arise because of the source of the data, the manner in which it is collected or due to the absence of guiding environ- mental indicators. In some cases it is collected in an ad hoc manner from books, reports or downloaded from the Internet. Most data is acquired from other organisations, presumably those which have that particular data man-
Quality and accuracy of data
Quality and accuracy of data and completeness or non-existence of datasets are important because they impact on the subsequent reliability and use of secondary information and other derived products. The most common limiting factor regarding the existing datasets was the quality and accuracy of the data. Four fifths of the institutions cited it as a problem. This is also a challenge at the sub-national level where district environment officers use information from the sectors (Imirenge) to compile their reports. For exam- ple in Nyamagabe district issues surrounding the accuracy of submissions from sectors at times necessitates extra trips by the environment officer to cross-check the data. Further, the reports from the sectors could be more integrated and employ standard usage of units of measurement (Ndayitabi 2009). This is shown in Figure 3.
Completeness of data was the second most common problem being cited by more than two thirds of the institutions. Additionally six per cent of insti- tutions indicated that total absence of data was a problem.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48