Monthly Archives: October 2015

Skills Learned from GIS Labs

In Lab 1, we learned how to choose appropriate data and to be critical of the sources from which we obtain our data.  Building on this skill in lab 2, we learned how to compare metadata, geographic datums, geographic coordinate systems and map projections, allowing us to make sure we are using our data correctly.  This is a very important step in GIS mapping, as in order to create an accurate map in ArcGIS, data must be within the same coordinate system, and if this is not the case the coordinates on the map will not be plotted correctly.  In addition, making sure that the map projections are all the same so that they are all updated to the most recent projection system will increase the accuracy of the map.  By combining these skills of critical analysis of data sources and ensuring the properties of the map are aligned with each other, I now know how to create an accurate, up to date map using reliable data.

Another skill I have learned throughout the labs is gaining an understanding of remote sensing, such as Landsat data, and how this can be effective in ArcGIS.  By using Landsat data within ArcGIS, this enabled me to group layers together to create a map analysis of Mount St Helens before and after the volcanic eruption in 1980.  By creating layers of images of Mount St Helens in 1979 and in 2002 using Landsat data and changing the colours of each layer, I can turn on and off these layers to analyse how the surrounding landscape has changed.  This is a useful skill as it could be used to present a map to environmental organisations who are analysing how the topography and land use has changed around Mount St Helens since 1980.

In lab 3, I found it particularly effective to use the intersect tool in the ArcToolbox to combine data from the areas of Vancouver which were in a Tsunami danger zone and data of where schools and health facilities are located in Vancouver.  Although keeping track of the intersected layers was confusing at first (what layers had I intersected? What units are in the attribute tables?), this is a highly useful skill for presenting a map to companies or local governments to show what defense mechanisms should be set up to avoid flooding of danger zones.

Lab 4

For this lab, it was required to obtain data by ourselves instead of relying on the ‘get data’ source provided by the GEOB 270 TA’s.  In order to do this, I had to download data from Abacus in order to obtain the shapefile for the area I wanted to map.  Further, in order to collect Census data from the Census tract I wished to map, I accessed this information on CHASS.  This process allowed me to gain the skills to import data into a GIS map I have sourced myself, and the end result was a map which showed housing affordability in Vancouver compared with Montreal.  The skills I learned in this lab included changing classification methods, and how this changes the data you have collected in how it is presented and interpreted on your map.  I also understood the importance of metadata when downloading data online, as it informs the analyst what previous editing or analysis has been performed on the data, which could potentially impact the results of your final GIS analysis.

Lab 5

In Lab 5 I was given the task of mapping an environmental impact assessment of a ski resort at Garibaldi Lake.  In order to do this, I was required to perform a select by attributes function to calculate the area which is inhabited by a number of different redlisted species.  As I had collected data on different endangered species, I had to use the Arc Toolbox to perform a merge on the different species in order to create a polygon which expressed all of the endangered species.  This way, it was much easier to calculate how much area is inhabited by redlisted species as they have all been grouped together.  Further, I was tasked with displaying information on stream riparian zones.  In order to do this, I performed a multi-width buffer analysis on the stream layer.  This is a particularly useful skill as it allows the consultant to calculate, not only the area of the rivers, but the area of their 10 meter riparian zones.

What percentage of Vancouver is in a tsunami danger zone?

To calculate what percentage of Vancouver is in danger, I firstly opened my attributes table for the layer Affected_Landuse (Figure 1).  I then added up all of the total areas in meters for each of the categories, which gave the value 14862774.11 meters. I then found the total area of Vancouver in meters by looking at the attribute table of the Vancouvermask layer, which gave the result 131033339.95 meters.  I therefore found the percentage of Vancouver that’s in danger by:

Total area in danger = 14862774.11/131033339.95 * 100 = 11.34% of Vancouver is in a tsunami danger zone.

Figure 1:

What educational and healthcare facilities are within the tsunami danger zone?

In order to find the educational facilities which are in a tsunami danger zone, I used the ArcToolbox ‘intersect’ feature.  I intersected the layers Danger_Tsumani_Landuse and Vancouver_Education, and in order to do this I inserted these layers to the input box.  I named the output feature ‘Education_within_dangerzone,’ which therefore gave me the educational facilities within the tsunami danger zone.

I repeated this process to find the healthcare facilities within the tsunami danger zone, so again I intersected the Danger_Tsunami_Landuse layer but this time I used the Vancouver_Healthcare layer, and named the output feature ‘Healthcare_within_dangerzone.’

Healthcare facilities within danger zone is seen by viewing the attribute table of the layer Healthcare_within_dangerzone, and Education facilities within danger zone is seen by viewing the attribute table of the layer Education_within_danger.  The results are seen in the table below:

Fixing Misaligned and Improperly Referenced Spatial Data

Coordinate systems give you a frame of reference to locate features and align them with each other.  When data is projected into a different coordinate system, the linear unit also changes, along with area, shape and distance.  When this occurs, coordinates can be placed in the wrong location, decreasing the accuracy of the map. To ensure this doesn’t happen, we must make sure that the coordinate systems are the same.

For each of the layers, I would right click and select ‘properties.’  I would then click on the ‘source’ tab and scroll down to the ‘Geographic coordinate system’ section.  Here, I would check that each layer has a coordinate system and a datum, and if not I would add one to layers that are missing them.  It is vital to make sure the coordinate systems and projections are the same for each of the layers to ensure that each of the layers is correctly coordinated with each other, and data will not be incorrectly located. To do this, I would go to the ArcToolbox, click on projections and transformations and then click on define projection.  I would then ensure that each of the layers are using the same projections. Secondly, I would review the unit of measurement for each of the layers. The layers must be in the correct order so that you can see all of the data you wish to present.  For example, if creating a map showing the major cities in Canada, you must ensure that the layer showing the provinces of Canada are on the bottom, so that major cities can be placed on top and can be seen.

Advantages of Using Remotely Sensed Landsat Data

Using Landsat data is a key tool for geographers to remotely sense how much change has occurred in an area between short 16 day intervals.  This allows geographers to analyse how areas have changed for example before and after a natural disaster, such how flooding impacted New Orleans after Hurricane Katrina in 2005.  With this data, risk assessment can be done to analyse what defence systems should be in place and planning how to rebuild the city.  As this data dates back to 1972, there is a lot of information to be worked with and a vast number of research can be done with this data.  This catalogue of data could be used to map the retreat of glaciers from 1972 until the present day.  Landsat is particularly useful in generating data of areas which are difficult or impossible to reach, allowing geographers to understand and map these areas.  Importantly, as this data is free, it is not only accessible to geographers with a high budget for their research, and it means geographers do not have to travel to these areas to collect the data themselves.  Landsat satellites can display infrared and visible light, so we can analyse data which would not be visible to the human eye.