Week 3.2 - Projections
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiKUqKVCN6x70tUPWOg4bZI8fcrONqA4DrIAwfDtllUzw13YKbAqcUQ2OaA8SveT17xNX9vdBms3dj8o8_osJplPRbNJ7o9HWv7WRyO0auQIu6VhnKGBZhW3Rwjj9Yfhy5PBwI1cLG1GA0/s640/Week3Lab_JaneenCayetano.jpg)
This is my least favorite thing to work with and to teach to staff in our organization. I find projections really hard to define especially with most publicly available datasets not having metadata. It's quite easy to explain on what projection really means but questions about what's the best projection to use is really hard to decipher. Since, I work on a lot of countries it's quite hard to determine the best projection to use. The important thing that I kept in mind is that if your doing a spatial analysis in your data make sure to have a consistent projection across all your layers. Like what was shown on the map, you'll not be able to notice the difference until you move in to a closer look at your visualization. In addition, digging deep through your dataset calculations would also provide you more clarity to the advantages and disadvantages of each projection. Going over the sample counties gives you a grasp on the difference between the...