GIS CLASS TEST
GIS is a set of tools for storing, collecting, transforming, and displaying real world data for a particular set of purposes
Spatial data=data for any space
Geographic data=data for the earths surface and near surface
Geospatial data= a subset of spatial data specifically applied to the earths surface and near surface
Location is a key organizing principle of geographic information and extracting data
GIS and maps share a layer based view of reality
Georeferencing= the process of establishing a relationship between real-world and map layers and is achieved using a spatial coordinate system that uses projections to flatten 3D to 2D
Data Management
A geodatabase (.gdb) is a key database native to arcgis
Allow functions to run quicker
Have higher integirty
Some functions apply specifically to data within .gdb
Good for data management as they store all data in one single location
Shapefile (.shp) is another older data file native to arcGIS that is used for feature based data
Catalog view- makes it easier to manage data compared to browser view
Metadata is descriptive information about data (good metadata in digimaps but not arcgis)
Analysis tools
Geoprocessing- covers wide range of functions called tools
Feature selections- analysis tools for when you want to work with a particular subset from a feature class dataset
for the automation of standard repetitive sequences
for the modelling and analysis
Proximity toolset is an example of geoprocessing- used for asking questions about whats near an input set of features
Buffer tool- establishes an area around specified inout features (point line or polygon), output produced is always a polygon
Attribute feature selections- structured as precise, logical expressions, following the rule of syntax SQL (structured query language). Can add a connector (AND OR) to base the feature selection on one or more attribute
Spatial feature selections- the selection of features in one layer based on its relationship to features in another layer
Once a subset of features is selected you can refine the selection by narrowing the selection, expanding it or inverting the selection
Feature data creation
Digitizing- a set of arcGIS tools for creating and editing features to reduce error
Important to digitise as the real world changes continuously therefore its important to maintain the currency of datasets within GIS
Important to create data when emergencies strike, in data poor regions
To capture information based on historic features
Extracting geometric measurements from features
Geometry measurements can be extracted from points (centroid centrie of a polygon) to create a new representation of the same features using the maximum x, y coordinates
Creating point features
Coordinate data is ubiquitus from paper maps to gps that all can be used within GIS
Georeferencing is the process of establishing the relationship between GIS dataset and its real-world location, accomplished using a spatial coordinate system
Coordinate system is part of the map properties. If dataset has different coordinate system arcgis will try to convert it, if not sufficent info may be issues with drawing it correctly. Two different types of coord systems
Geographic coordinate systems are a scale down globe like representation of the earth, locations are measured in latitude and longitude angles on a sphere
Spherical coordinate systems hard to work with
Along only the equator does the distance measured by a degree of longitude approximate to the same distance covered by a degree of latitude
Projected coordinate systems are based on a map projection from a transformation of a 3D sphere to a 2D cartisian plane
Projections used to overcome measurement difficulties with geogrphic coord systems though some degree of distortion when collapseing a sphere into a flat plane
OS national grid is a projectedcd coord system that uses the transverse mercator map projection (cylindrical). It has a true origin on central meridian and latitude of true origin 49 deg N. Has a flase origin 400km west and 100km north of the true origin. Referencing agaisnt the false origin ensures all locations in britian defined the BNG are measured in positive coordinates
WIKD = well known ID a way to idenitfy a particular spatial reference system
Attribute table joins
Good as it can be unrealistic to expect to always get attributes of interest in one ready made gis dataset , they may also be in other formats (spreadsheets, csv files etc)
It isnt efficent to store all attributes of interest in one single dataset as it can make them very large, slow to search and process- good database design is to make links within a database tables when necessary
Simplest example of a table join is a one-to-one table join, with one common field within the tables. Join direction is important, a table join is temporary but can be permanently saved by exporting the feature dataset to create a new one
within table joins can either keep all records or keep only matching records within tables
Many to one joins can be used in situations where the common field has duplicates of one or more variables in one table and unique values within the other
other joins for more complex joins such as many-to-many, one-to-many
Choropleth mapping/graduated colour mapping is used to make thematic maps from socio-econmoic data. To effectively make these maps we must understand the data and the mapping method
Understanding the data
Area-aggregated data is data summaried for sets of areas within which individuals are recorded. Individuals can be persons or units and typically incude socio economic characteristics
Area aggregated data often hard to work with as it is difficult to know what set of area to aggrgate the individual level data to as multiple sets are possible by changing the number of areas (scale), or layout (zonation). This can yeild different resukts and is knownas the modifyable areal unit problem
Assumed that aggregate data follows a homogenus distribution (values for given area applies the same everywhere)
Counts of individuals are summary statistics (if you change the size they become spatally extensive), but if you change these values to be expressed as densities, percentages or rates they dont depend on szie of the area ( are spatially intensive)
Conversion of spatially extensive to insentive is important to derive values appropriate for thematic mapping (e.g to density, percentages, rates, proportions)
Understanding the mapping method
principles of the cholopleth mapping technique.
- Obtain or derive values to the map (spatially intensive)
- Order the values from smallest to largest
- Classify the ordered values into smaller number of ordered datasets
- Apply an ordered sequence of coloured hues corresponding to ordering of mapping classes
a. lightest hue=bottom class
b. darkest hue= top class
Different data classification methods to use within ArcGIS
Natural breaks- minimises within class differences and maximises between class differences. Good for exploratory purposes as its based on the distribution of data. But class breaks may not be intuative to map readers
Quantile- Places the same number iof data values in each class. No classes are empty but may put different values in same class and/or similar values in different classes
Equal interval- all classes cover the same interval. Easy to read and good basis for comparing maps, doesnt account for the data distribution as all value smay fall into just a few classes and coul db eclasses with no values
Manual- class breaks set to predefined values. Sometimes imprtant to set values but may result in less detialed maps
Layout is an arrangement on a page/screen that includes maps, ledgend, scale etc. Important to consider the cisual heigherachry, allingment and balanvce with its purpose and audience, reolution and quailty of map
DTM= Digital terrain model is a generic term for datasets that contain measuremnts of a characteristic of earths terrain in a raster formatio such as land and height
DEM= Digital elevation model (bare earth model) is usesd interchangably with DTMs
Contrast sketches can be applied to terrian models
DSM= digital surface model includes the height of features on the ground such as bukdings etc
tiles within raster models can be mosaic-ed together into a single dataset
We can use the spatial relationships in a DEM to produce other terrian surfaces
Slope surfaces - To calculate this, 3x3 neighborhood around cell considered and application of trigonometry to rise over sun (the rate of change in the vertical vs horizontal axis)
Aspect surface- identify the section of maximum slope for each grid cell in the input DEM, aspect values are compass angles measured clockwize from due north (0/360)
Viewshed surface- Assessment of the sline of slight visibility of cells on an elevation surface, from one or more observer locations (features).
Can set an offset height for the observer location
Solar illumination / energy surfaces
3D scene visulaisation is useful for viewing variations in elevations and other surfaces, useful for public engagement and communicating impacts of new developments due to the added degree of realism
In layer based view (2D) representations azre organised into one of three types of entities using sets of x, y coordinates; points, lines, polygons. Which can form surfaces pts, lines, polygons), and networks (pts, lines).
These basic entities can be used within GIS by two different data models- vector and raster
VECTOR DATA MODEL
Vector data models use 2D cartisian x, y coordinates to store shapes of entities. More complex shapes require more points to represent them as points are the basis from which other entities are constructed
Individual locations= single x, y coordinate
A line (polyline)= a connected series of points
Polygon= a closed loop line
Associations between discrete objects and vector data models recording attributes using a regular tabular structure
Points= 0D, lines = 1D, areas=2D, volumes = 3D, times=4D
data structured concerned with information computers require to (re)construct spatial data models in digital form. Includes info on location, shape and realtions amoung features and their relative positioning to one another (topology (connectivity, adjacency and containment is part of topology))
Vector spatial data structures often in a table like format (simple list of coordinates representing points, lines, polygons). Although often not efficent as coordinates along common boundaries between neighbouring polygons have duplicates, allowing coord sharing can help but still issues
Including topology, with the minimum data, to avoid exessive quantities and costly processing times/overhead times is the central challenge for spatial data structures
Fuller vector topological spatial data structure includes encoded information on:
- Constituent point coordinates (designated nodes)
- Line segments (arc or chains) with line direction encoded by a start or end node
- Polygon adjacency by storing info on the left and right side of the polygon for each line segment
- Line segments making up boundary of each polygon
- Attributes
ArcGIS is a vector topological system and adopts the term feature for vector encoded objects (point feature, line feature, polygon feature)
Feature class= a collection of features sharing the same geometry type and having a common set of attributes (vector dataset)
Spatial scale is an important aspect of shrinking the real world to managable sizes and quantities of information. Scale is the ratio between map and real world size
Large scale mapping provides large amounts of deatil but for small areas e.g 1:1,000
Small scale mapping provides a small amount of detial over larger areas e.g 1:100,000
Still issue of scale within arcGIS as when viwing over a large area detail is generalised
RASTER DATA MODEL
Raster data models are a regular array of measurements most commonly grids of square cells. Grid cells are the bulding blocks for holding representations of entities, with the shape of entites being determined by the groupings of cells
Most appropriate for representing the real world phenominon which vary continuously ( can be represented as continuos field of values). Also used within imagery from air/space sensors
raster properties
Resolutions (spatial, temporal, radiometric)
Levels of measurement of data (pixel type)
Raster datasets often have a fixed cell size (spatial resolution)
Smaller cell size= higher resolution=higher feature spatial accuracy=slower display=slower processing=large file size
Larger cell sizes= lower resolution=lower feature spatial accuracy= slower diaplsy=slower procesing= smaller file size
Spatial resolution: Higher spatial res= smaller cell size= more detail
Spatial scale: Higher spatial scale= more detail
Important consideration for raster data on contonous fields
Nominal=categories
Ordinal= ordered categories
Interval= Differences between measurement , no true 0
Ratio= difference between measurements, has a true 0