Saeler week-2

Chapter 1
-GIS has grown greatly even offering new sources (Lidar and drones).
-Greatest change for better is larger amount of people doing spatial analysis and sharing results offering more data than ever
– GIS data easily accessed by sites such as ArcGIS Living Atlas of the World
– GIS analysis is a process for looking at geographic patterns in your data and at relationships between features
– To start gis analysis figure out what data you might need then think through a question (usually). Try to be as specific as possible. Also consider who will be using data and how they may be using it to flush out your question or data you’re seeking. Than understand what data you need and select the method of analysis accordingly. Finally analyze your results.
-Types of features are discrete, continuous phenomena, or summarized by area
–Discrete- the actual location can be pinpointed at any given spot the feature is either present or not
–Continuous Phenomena- such as precipitation or temperature can be found or measured anywhere. They blanket the entire area you’re mapping. Can determine a value at any location. (weather station)
–Features summarized by area- represents the counts or density of individual features within area boundaries. (for example number of businesses in a zip code). data value applies to the entire area but not a specific location within it.
-2 ways of representing geographic features vector and raster
— Vector model each feature is a row in a table and are defined by x,y locations (address of a business/location of a monument)
— Raster model, features are represented as a matrix of cells in a continuous space (i think weather radar maybe?)
–discrete features and data summarized by area are usually vector model
-Map projection and coordinate systems
–translates locations on the globe onto a flat map. All map projections are distorted. Which distortion can be negligible on small scale but can cause problems when mapping a larger area.
—If collecting data from multiple sources ensure that map projection and coordinate system is the same.
-Understanding Geographic attributes
— Every feature uses multiple attributes including- Categories, ranks, counts, amounts, ratios

—Categories are groups of similar things (help organize data roads can be classified as freeways, highways, or local roads). Ranks put features in order from high to low(nonspecific in value but tells you the order of features such as one may be ranked lower than another but you won’t know how much). Counts and Amounts show total numbers. Counts are number of features on a map. Amounts are any measurable quantity relating to a feature (amount of trees in a thicket of woods/area). Ratios show relationships between two quantities and are created by dividing one quantity by another (dividing amount of employees by number of business locations give avg number of employees per location).
-Working with data tables- common operations performed on features and values within tables are selecting, calculating and summarizing
–Selecting is to select features to work with a subset or to assign a new attribute value. Calculating is to calculate attribute values to features in the data table. Summarizing is for getting specific attributes to get statistics.

Chapter 2

• Deciding what to map
  • Map the features you are focused on such as a police department mapping crimes
• Preparing your data
  • make sure all features have geographic coordinates assigned before mapping
    • when bringing in data from another program or when entering by hand features will need specific locations such as the longitude and latitude coordinates.
  • Assigning category values
    • each feature requires code that identifies its type (sandwich: philly, blt, cbr, etc.)
    • to add a category you create a new layer in the layers data table and assign appropriate value to each feature
      • many categories are hierarchical with major types divided into subtypes
• Making your map
  • Features displayed and which symbols to use to draw them
  • mapping a single type- use same symbol for all features (may suggest differences in feature)
  • using subset of features- can map all features in data layer or subset based on a category value. (can reveal patterns that aren’t apparent when mapping features)(more commonly done for individual locations)
  • Mapping by category- map features by using different symbol to draw features for a different category value. (gain understanding of how a place functions)
  • Displaying features by type- Features can belong to multiple categories and using different categories can reveal different patterns.(can usually display all categories on same map but if features are to close or complex create multiple maps)
    • displaying a subset may show a relation between categories better(no more than seven categories/patterns)
  • Grouping categories- can group categories to limit patterns making it easier to see relationships between features. 
  • Choosing symbols- if mapping individual locations use single markers. use variety of shapes and colors or patterns to help distinguish features making relationships in categories and features easier to recognize
  • Also be sure to map noticeable reference points to make locations more recognizable for other people going over your data such as famous buildings and memorials  or roads such as highways.
  • Analyzing geographic patterns
    • clustered distribution- features more likely to be found near other features
    • uniform distribution- features less likely to be found near other features

Chapter 3

• mapping most and least- map features based on a quantitative value. Mapping features based on a quantity can reveal a more specific pattern such as instead of mapping cars in a town going into detail and mapping specific brands or types of cars reveals more in depth detail you may be looking for 
• What do you need to map?
  • by mapping features with similar values it allows you to gain a better understanding of your map 
  • you can map quantities  with discrete features , continuous phenomena or data summarized by area
    • discrete features can be individual locations, linear features or areas
      • individual locations and linear features usually represented by graduated symbols
      • areas shaded to show quantity
    • continuous phenomena can be defined areas or a surface of continuous values
      • defined areas- graduated colors
      • continuous values- graduated colors, contours, or 3d perspective view
    • Data summarized by area is usually shown by shading area based on its value or using charts to show value in an area
  • Creating classes
    • once you’ve determined your quantities determine best way to represent them by assigning each individual value its own symbol or by grouping values into classes (trade off between presenting the data values accurately or by trying to get the most accurate map)
      • counts and amounts and ratios are usually grouped into classes 
      • ranks  are to be mapped as individual values since they are not continuous 
    • Mapping individual values lets you find patterns easier 
    • using classes lets you see features with similar values
    • Use standard classification schemes if you want to group similar values for easier pattern recognition
      • natural breaks-data clusters are placed into a single class and breaks happen between clusters of value
      • quantile- each class has an equal number of features
      • equal interval- each class has an equal range of values(difference in high and low value is the same for each class)
      • standard deviation- each class is defined by its distance from the mean value of all features
      • when dealing with legit outliers put them in their own class, group them together in a class, group them with the adjacent class, or designate a special symbol for them
    • Making a map
      • GIS provides graduated symbols, graduated colors, charts, contours, or 3d perspective views