Data-Normalization-Classification/slides.html at master · ubc-lib-geo/Data-Normalization-Classification · GitHub

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      <section>
        <h2># Data Normalization & Classification</h2>
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        <h3>Learing Outcomes</h3>
        	<p>1) Investigate how data normalization impacts the way we perceive patterns in a dataset </p>
            	<p>2) Look at different data classification methods and how they impact the way we perceive patterns in a dataset </p>
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        <h3>What is Data Normalization</h3>
        	<p>Normalization, is the process of scaling (AKA Normalizing) one number by another.  For example, we can as the question: How many people do Canadian Police kill per year? We need two pieces of information:
			<br>A) The number of victims of police violence
			<br>B) The lenght of the period of record
			<br>Dividing A by B, this will tell us the average number of police killings per year over the period of record.</p>
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        <h3>Data Classification Methods</h3>
        	<p>We can use different classification methods to highlight different patterns in a dataset.  The ones we'll focus on today are:
    		<br>* Equal Interval
			<br>* Quantile
			<br>* Natural Break
			<br>* Standard Deviation
			<br>* Manual Breaks </p>
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