Geographic Data + Mapping

Landfill sites in Ontario was chosen because I was interested to see the spatial patterns of these locations. The dataset was published by the Government of Ontario on March 12, 2020 on their website. The information includes 20 large landfills found in Ontario regarding site name, address, landfill type, service area, total site and fill rates. The following data could be used for urban planners and municipal waste management to oversee the control and yields of waste from Ontario cities and towns. With the rise of urban population, municipalities must be prepared to handle growing amounts of waste to be sent to landfills while considering the total site area of these landfills and their maximum capacities and their overall environmental impact. 

After completing the dataset on Microsoft Excel, the file was placed into Geocodio.com for geocoding. The website provided a map with the locations and an updated file with the geographic coordinates.

Geocoding is the process of converting a street address or place name into its latitude and longitude coordinates. Spatial datasets may present locations with text based descriptions however these are not compatible with geographic information systems, therefore they must be translated into coordinates to be used as machine readable location data.

"Millions of people around the world are still not represented on maps. Voices of local communities are unheard; they are left out in access to basic services like health or education, and are not included in relief operations when disasters strike." - HOT OpenStreetMap Team, 2019

For the MissingMaps Project, I chose task #899 of the COVID-19-Palapye District - 01 in Botswana. Task #899 is noted as a red priority area in the town of Palapye. The Botswana Institute of Geomatics (BIG) requested the Humanitarian OpenStreetMap Team to map buildings in the Greater Gaborone area in Botswana to help provide a base map for COVID-19 monitoring tools for their government.

__________________________ _______Data for October 29th, 2020

As seen in maps above, there is a drastic difference between the two maps. Map three used the natural breaks method whereas map four used the quantile method. In the natural breaks method, the data was separated by distinct break points formed by an algorithm to group values in classes. An individual who wanted to deceive an audience to convince them the whole pandemic is under control could show them map three where the world map displays countries in lighter hues of red on the colour ramp. Upon first glance on the natural breaks method map, it appears as if there are less cases per country around the world however, this is because there are ranging values across the dataset. On the other hand, the quantile method map presents itself as a more alarming map displaying more countries coloured in the intense red, the darkest colour on the colour ramp, indicating the highest range. In this map, there was an equal number of observations into each class. Although both maps contain the same data, they are contrasting. Data classification is important because it can help visually display important trends of variables of interest in geographic areas that can be used to gain further insight in all fields. However, it is important to be aware of any bias that may form from choosing an ineffective classification method with the type of data being used

As seen between the raw count mugging map and mugging rate map, there are stark differences shown because of the two different mapping techniques used in the choropleth maps. The data was normalized and converted into a mugging rate per 10,000 people of a neighbourhood to gather more meaningful results to compare the rates between neighbourhoods. The raw data does not take into consideration between neighbourhoods and their varying populations that can factor in the number of mugging counts for instance, a neighbourhood may have a higher count than another merely because chances increase with a significantly higher population. It is important to understand how the data is presented because it will make an individual aware of how the map may present skewed information. Maps are very powerful visual tools. It may deceive people upon first glance if the viewer does not understand the mapping technique. 

The raw count map appears as if there are many dangerous neighbourhoods in Toronto based off of the number of neighbourhoods being shaded in the dark colour, typically indicating the highest class with the highest count. However, when the data is normalized then mapped using rates, the map displays only two neighbourhoods in the top class with the highest rates of mugging compared to the twelve in the first map. For instance, in the first map, the neighbourhood of L’Amoreaux is classified in the highest class of muggings with a raw count of 23 counts with a population of 43, 993. It is classified in the same category as the neighbourhood Moss Park with 59 points with a population of 20, 506, yet Moss Park has nearly half of the population of L’Amoreaux and more than double the mugging cases. When the data was converted into a mugging rate per 10,000 people, the second choropleth map became more useful as the information took into account the important factor of population. L’Amoreaux is now placed in the second highest class and has a mugging rate of 5.23 muggings while Moss Park has a rate of 28.77 muggings in the first highest tier.