Administrative notes regarding lab exercises and schedule

• Lab #1 is due on Stellar before class next Monday, March 1
  
• All Office Hours:and Zoom Links are listed in 'Materials' section of Stellar:
• https://stellar.mit.edu/S/course/11/sp21/11.188/materials.html
• Joe Ferreira: Thursday 10:45 - 12:15
• Rounaq Basu: Tuesday & Friday 12:30 - 2:00
  
• Lab Exercise grading (25% or semester grade)
  • we aren't going to grade every detail
  • you'll get a 'check' = okay, 'check-plus' = especially good, or 'check-minus' = not as complete hoped.
  • we will post lab exercise comments after each lab is graded
• Lab Exercise purpose:
  • quick start with basic GIS tools and features
    
  • highlight important ideas and methods
  • assist you in becoming more self-sufficient with QGIS help pages
  • as semester progresses, they will be less cookbook and a little more open ended
  • Don't just push the buttons to get the 'right' answer - pause to think about what you are trying to do, what info/tools are needed, why QGIS is organized in a particular way, and why that way may be different than you originally anticipated

Outline of Today

• Cartography: Map elements and thematic mapping issues: symbology, classification, color theory
• Projections and coordinate reference systems

• GIS Example:  Site Selection for Low Cost Grocery Store Chain
• Powerpoint slides used by commercial firm to market site selection tools
• by Edens & Avant and RPM consulting

• Database concepts
  
• Relational Database modeling
• Structured Query Langauge (SQL)
• Joins and Relates in QGIS

Map elements and thematic mapping:

Symbology, classification, and normalization

 

Scale

Ratio Scales

            1:10,000, or 1:100,000 or 1/100,000
            One unit on the map equals designated number of units on the ground.

Verbal Scales:

     One inch represents 2,000 feet
(1:24,000 because there are 24,000 inches in 2,000 square feet).

     One centimeter represents 20 kilometers (1:2,000,000)

Printout vs. onscreen:

10 foot pixel + 72 pixels per inch onscreen

   ==> One inch represents 720 feet (1:8,600) -

But some screens have higher/lower pixel densities; not all screens have square pixels; also, screendump to printer will change scale since printer will have different dot-density than the screen

Beware! Good GIS software will try to match screen and printer properties to software settings so screen and printouts will show appropriate scale and show correct scalebars. For the scale to be meaningful, display units and hardware choices must be properly identified.

Large Scale or Small Scale

In general,

         Large scale: >= 1:24,000 (the representative fraction is large, but they are good for *small* area representation - city block)

         Small scale: <= 1:500,000 (good for *large* area representation - metro area)

  Important concept: “Minimum Mapping Unit" (MMU)

• The smallest feature chosen to map (anything smaller is merged)
• Example: Oregon GAP Vegetation Map
  • based on 30m Landsat7 data - choose 1 ha MMU

 

Typical Scales Used

In Metric System:

1:10,000 (example: German national basemaps – individual houses shown) 1:25,000 ... 1:100,000

In American System:

1:9,600 (one inch represents 800 feet)

1:24,000 (one inch represents 2000 feet) – typical USGS “quad sheet”

1:62,500 (one inch represents slightly less than one mile)

 

Key Cartographic Principals

Maps are a medium of communication

              So…know your audience

              Or if you don’t/can’t, use the “10 foot rule”

                     Can an average citizen read & comprehend E sized plot at 10 feet?

                     Or a page printed at 8.5x11” – font size at least 12 point

A good map should never be a “GIS Data Dump”

Selective emphasis is key

The hard part is removing data, when in doubt - delete

Divide mapped elements into “figure / ground”

Figure = Foreground elements – the essential story of the map

Ground = Background elements – provide context, but don’t overwhelm

==> Use visual symbols, colors, and shades to emphasize foreground, & minimize background clutter

Colors and Categories

Human cognitive limit / rule of thumb:

Try to limit the number of thematic colors

• If not possible, try creating logical visual subgroups
  • example: housing in 3 shades of orange, commercial 3 shades of red, etc.
• Hard to distinguish more than 3-4 tones within same hue
  • Even harder when your map is reproduced in grayscale…

Typical GIS color defaults are fully saturated colors

• Tip #1: desaturate colors, especially background colors or those covering large map areas
• Tip #2: reserve bright, saturated colors either for foreground elements, or small polygons
• Tip #3: turn *off* or 'dim' the outline of polygons, particularly for background polygons or small polygons

Six Principal Visual Variables

• Size, Shape, Graytone Value, Texture, Orientation, Hue
  • How can you use these dimensions to symbolize points, lines, polygons?

Use contrasting symbols to portray geographic differences

For qualitative differences

Use shape, texture and hue (e.g., land use types).

For quantitative differences

Use size to show variation in amount or count
(e.g., population, number of crimes),

Use graytone or hue to show differences in ratio or intensity
(e.g., proportion of household in poverty, population density).

 

Southern New England Counties

• Thematic mapping (again) - simplest display of spatially varying phenomena
• Note use of QGIS 'help files' regarding thematic map types & classification choice

• Layouts: Features of a good map (lots more to good cartography - see references)
• Title, Legend, Scale Bar, North Arrow, Data sources
• Your name or organization
• Other feature labels and annotations, including classification method
• Best general reference on Graphic Communication (beyond cartography)
• Edward R. Tufte, The Visual Display of Quantitative Information
• For more, see online help and class readings on cartography

5-minute color exploration exercise:

Cynthia Brewer's ColorBrewer 2.0: https://colorbrewer2.org/

• Notice how different the map looks depending upon choices
• What of her advice do you like?
• Be sure to click some of her information (i) buttons on the main page
  

Map Projections and Coordinate Systems

Readings: Paul Bolstad, GIS Fundamentals, Chapter 3, especially pages 116-136

Datums, Map Projections, & Coordinate Systems

      What is the minimum information needed to precisely determine
      location on the surface of the planet?

Need *both* a known coordinate system
and a known model of the earth’s surface

If you only know one, you can be hundreds of meters off target

      -literally

An Ellipsoid or a Datum are abstractions of the surface of the earth

WGS84 (the World Geodetic System of 1984) is the most commonly used standard ellipsoid.

Lat/lon coordinates using this ellipsoid can also be referenced as EPSG:4326 where EPSG = European Petroleum Survey Group which maintains a geodetic parameter database for thousands of coordinate systems.

In North America, the most detailed and recent ellipsoid is called the North American Datum of 1983 (NAD83) (the earlier version is NAD27).  WGS84 used similar methods worldwide but uses fewer North American reference points than NAD83 did within North America.

Geographic Reference System: Latitude and Longitude

Axis: the center of earth rotation.

Equator: The plane through the center of mass perpendicular to the axis.

Longitude: lines slicing the earth parallel to the axis, and perpendicular to the plane of equator.

The line going through Greenwich in London has 0 longitude.

Range from 0 to 360 degrees, or 180 degree west (-) to 180 degree east (+).

Latitude

Latitude is defined based on an ellipsoid representing the shape of the Earth's surface.

See: Prof. Peter Dana's notes on projections and coordinate systems ( U. of Colorado ) http://foote.geography.uconn.edu/gcraft/notes/coordsys/coordsys_f.html

<Click the images below to enlarge...>

Latitude definition:

A line drawn through a point of interest perpendicular to the ellipsoid at that location, the angle made by this line with the plane of Equator is the latitude of that point.

Ranges from 90 degree south (-) to 90 degree north (+).

 

What do Latitude and Longitude mean?

Two points on the same longitude, separated by one degree of latitude are 1/360 of the circumference of earth apart, or about 111 km apart.

One minute latitude is 1.86 km.

One second latitude is 30 m.

For the same latitude, one minute of longitude separation corresponds to different distances depending on the latitude (111 km at equator, nothing at the poles!).

Nowadays, latitude/longitude are most often expressed in decimal degrees.

Distance calculation using latitude and longitude

• Latitude -90≤Ø≤90
• Longitude -180≤λ≤180
• Arc distance between two points on the earth surface (spherical):
  • R*arccos[sinØ1sinØ2+cosØ1cosØ2cos(λ1-λ2)]
  • R is the radius of the spherical earth
  • Wikipedia reference: http://en.wikipedia.org/wiki/Great-circle_distance

Cartesian Coordinate System

• Plot longitude / latitude values directly on a 2D flat surface. (graph paper)
  

Map Projections

• Map projections transform the curved, 3-D surface of the planet into a flat, 2-D plane. You can interpret map projections as the image that would result if a light at the center of a hollow earth were used to cast a land shadow on a sheet of paper that touched the earth in various ways.

• Transform a position on the Earth’s surface identified by latitude and longitude (Ø, λ) into a position in Cartesian coordinates (x, y).

• x = f (Ø, λ)
• Y = g (Ø, λ)

• Map projections necessarily distort the Earth and the map scale. 

  
  

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Illustrate using Lab #1 layers for Cambridge and Stamen background

• QGIS with EPSG=26986 (Mass State Plane, NAD83, meters)
• QGIS with EPSG=3857 (Pseudo Mercator, WGS84)
• Zoom out to extent of US lower-48 states
• Use County layer from US48states.zip on Stellar
• Country-wide view is very different but Cambridge-scale differences are subtle
• Mass State Plane has North straight up for Massachusetts, but not California
  
• Turn on Stamen basemap
• note delays, timeouts and error messages
• basemap is image, full of individual images that need transformation
• 'world' view is very different but Cambridge-scale differences are subtle
  

NOTE: There is no need to memorize the formulas or properties of various coordinate systems. Just understand the concepts and how to find more information about specific cases and coordinate conversion

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Map Projection Classifications based on preservation properties

• The conformal property, preserves the shapes of small features on the Earth’s surface (directions). This is useful for navigation. E.g., Mercator projection and Gnomonic projection.

• The equal area property, preserves the areas. This is useful for analysis involving areas like the size of a land parcel, e.g., Goode’s projection.

• Any projection can have either conformal property or equal area property, but not both.

 

Map Projection classifications based on physical surface models

• Cylindrical projections -- wrapping a cylinder of paper around the Earth, projecting the Earth’s features onto it, and then unwrapping the cylinder;

• Azimuthal or planar projections -- touching the Earth with a sheet of flat paper;

• Conic projection -- wrapping a sheet of paper in a cone shape around the Earth.

• All three types can have either conformal property or equal area property, but not both.

 

Unprojected projection: Plate Carrée or Cylindrical equidistance Projection

• Geographic Coordinate System (GCS) uses longitude as x and latitude as y.

• Heavily distorts image of the Earth.

• It does not have either conformal or equal area property.

• But it maintains the correct distance between every point and the Equator.

• Serious problems (distorted area, direction and other properties) can occur when doing analysis using this projection.

 

The Universal Transverse Mercator (UTM) Projection

• Projected by wrapping a cylinder around the Poles, rather than around the Equator.

• There are 60 zones. Each zone is 6 degree wide and wrapped along a particular line of longitude.

• The projection is conformal, the scale is the same in all directions.

• UTM coordinates are in meters, making it easy to make accurate calculations of short distances between points.

• UTM projections have more problems at high latitudes.

 

State Plane Coordinates and other local systems

• UTM is still not accurate enough for small area surveying.

• During 1930s, each US state adopted its own projection and coordinate system, generally known as State Plane Coordinates (SPC).

• Each state chose its own projection based on its shape to minimize distortion over the area of the state.

• Some states have more than one internal zone.

• The North American Datum 1983 (NAD83) is commonly used for SPC.

 

Converting Georeferences

• Two datasets can differ in both the projection and the datum, so it is important to know both for every dataset (and the data can be expressed in feet or meters with different origins!)

• Use coordinate conversion to combine datasets that use different systems of georeferencing. Keep in mind, changing projections means the system must convert projected coordinates back to lat/lon (geographic) and then re-project into another projection/datum.

• Convert into projections that have desirable properties, e.g., no distortion of area, for analysis.

More info on Coordinate systems and projections

• Coordinate Systems
• Peter Dana's Coordinate Systems Page
• Datums & Units
• Peter Dana's Geodetic Datums Page
• Projections
• Peter Dana's Projections Page
• Two Different Projections of North America
• Three Different Map "Projections" of the Conterminus United States

GIS Marketing Example:

(1) Site Selection for Low Cost Grocery Store Chain

• Conceptual Model
• Brainstorm criteria for "good" locations
• Case Study Example
• Factors used in actual Commercial Shopping Center Site Selection
• Powerpoint slides used by commercial firm to market site selection tools
  by Edens & Avant and RPM consulting  
• Think about these marketing slides?
• Is the methodology or analytic scope overstated?
• What considerations are omitted, shortchanged, badly measured?
• From whose point of view is the siting service helpful or hurtful?

(2) 5 minutes general discussion of questions

Relational Database Modeling

Limitations of 'flat-file' data model & motivation for relational model (for attributes)

• One 'flat file' data table for each map layer is too simple a data model
  • some houses in sales89 may have sold more than once!
  • but, map has only one dot per house
  • what sale price is correct? average, most recent sale, inflation adjusted average, ...?
  • need to create new table with one row per house and 'average sale price' column
  • OR, add new column to existing table and compute 'average price'
  • BUT, either way requires a new table - either the old one with a new column or a *new* table that must be joined back to sales89 in order to map average price
  
  
  • Common many-to-many issues
  • Parcels can be owned by one or more owner
  • Owners may own more than one parcel
  • Owners change when parcels are sold
  • How do you find all owners with more than one foreclosed parcel since 2007

• Strategy: Relational Data Model (i.e., linked tables)
  • Retain tabular model for textual data but allow many tables that can be related to one another via common columns (attributes)
    • Join (or relate) tables together as needed by matching appropriate columns
    • Note that rows often have different meaning across related tabled (may require aggregation such as sum(), average(), count(), etc.)
  • For multiple sales example:
    
    • create 'summary' table with one row per house plus columns for number of sales, maximum price, average price, etc.
    • then join summary table via house ID to the original sales table
    • QGIS implements this idea with 'virtual layers' and the use of SQL (structure query language)

 

Structured Query Language (SQL)

• To insert, modify, and retrieve (SELECT) data from the database (data manipulation).

            SELECT columns

            FROM tables

            WHERE row conditions and joins are matched

            GROUP BY non-aggregated columns

            ORDER BY certain columns

• Can get complex (subqueries in 'from' and 'where' clause, 3+ tables, ...)
• Can be especially powerful when rows in each table have different meaning

e.g., one row per house with average sale price vs. one row per unique sale

Demonstrate QGIS example to handle multiple sales

Create 'virtual layer' via SQL query and join to sales89

select address, count(address) sales, avg(realprice) avg_price
from sales89
group by address

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Last modified 23 February 2021 [jf]

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