Data, data, data

Data, data, data- The word on everyone’s lips. Without it, we have very limited amounts of work we can do and very limited amounts of the project deliverables we can complete. At the moment, the data captured in the field this year has been cleaned, formalised and added to last years data. This was done via a SQL script. The data was then saved as a .csv file so that it could be imported into QGIS. The problems lie 1) In the fact that the SQL code written from last year runs by using the GPS note as a way of mapping the points. Two of the 5 groups this year did not have access to a GPS and thus their points are missing from the .csv file. This wouldn’t be so bad if it wasn’t for the fact that about a quarter of the data is now missing. 2) The accuracy level of 3-5m is a real struggle because the house points are not very accurate at all. These two problems mean that no accurate spatial analysis can be done on the data, no accurate maps can be created and no accurate 3D model can be created. Darn…

The route we have decided to take is to manually digitise the houses in Alaska. This was done in QGIS by digitising the Google satellite image found in the web plugin. By manually capturing this data, it increases accuracy and thus allows for better analysis, maps and 3D model to be created. The down side though is that the digitised points contain no attribute data such as address data. For now we are leaving that problem for another day. For today, we are on the mission of creating our maps and 3D model in time for demo 1 in 2 weeks time.

Lessons learnt throughout this debacle thus far are to not rely solely on one person for the acquisition of the cleaned data. Rather clean and acquire the data as a class/1 person from each group therefore speeding up the process and allowing for everyone to be on the same page when it comes to the final data set.