MODULE 1 Flashcards
Process of searching a database to retrieve specific information that satisfies a specified set of criteria
Query or Querying
Process of obtaining a map layer by directly working on its features and attributes
Spatial Query
commonly used to query attribute tables in GIS
Logical Operators
Querying a GIS map of landcover map for the presence of specific land cover can be done using what? (example)
“Landcover”=”Built-up”
- “Landcover” is he specific ___ in the attribute table of the map
- ”=” is the ____
- “Built-up” is the specific ___
- Column
- Logical operator
- criteria
used along with the logical operators to query a particular map with compound expression or multiple criteria
Boolean Connectors
the analyst can put additional criteria on the query to look up for a built-up, forest cover within the map of interest. how can this be implemented?
“Landcover”=”Built-up” OR “Landcover”=”Forest”
used in order to look for the built-up and forested areas within the map
Boolean operator (“OR”)
(Logical Operator attributes)
Equal to
(Logical Operator attributes)
<>
Not equal to
(Logical Operator attributes) <
Less than
(Logical Operator attributes)
>
Greater than
(Logical Operator attributes)
≤
less than or equal to
(Logical Operator attributes)
≥
greater than or equal to
(Boolean connector attributes)
for the condition to be evaluated as true, the logical expression on both sides of the ___ must be TRUE
AND
(Boolean connector attributes)
The logical expression on one side or the other side of the __ must be TRUE
OR
(Boolean connector attributes) The logical condition on one and only one side of the ___ must be TRUE. If both logical expressions are true or both are false, the condition will be evaluated as FALSE.
XOR
Spatial analysis refers to the numerical values that describes features of geographic data which length, areas, shape, and distance and direction between objects
Measurement
In GIS, the distance between geographic objects (home & office) can be measured using the following:
- Euclidean distance
- Great circle
- Network Distance
length of the line segment between two geographic objects (A & B) calculated in cartesian coordinates (x & y) using the Pythagorean theorem
Euclidean distance (Pythagorean distance)
Pythagorean Theorem Formula
a² + b² = c²
* a & b are the sides of right triangle
*hypotenuse
shortest distance between two geographic objects measured using an arc along the surface of a sphere
great circle or spherical distance
commonly used by ships and aircraft in order to save time and money when navigating
Great circle distance
described as connected sets of edges and vertices
Networks
the places that are being connected by edges
Vertices
the centerline of by directional road, the direction of traffic flow, or even lanes in a multi-lane highway
edges
the point where a city or settlement starts, intersection or turns and stop in a particular road network or route (bus stops, schools, hospitals)
vertices
characterised by dense road network, travling from one place to another occurs in where?
Route
Calculation of distance between places (vertices) includes the length of all roads (edges) covered to complete what?
Trip
Influence the distance traveled from one city location to another
Different traffic rules and restrictions
Process of converting geographic data into a more useful product that can be the basis of different application based on simple rules
Transformation
Common transformation methods applied in human settlements planning
- Buffering
- Points in a polygon
- Polygon overlay
- Process of generating a buffer polygon around a point, line, or polygon based on user-specified buffer distance
- one of the most useful transformations in GIS environment
Buffering
Danger zones from fault lines, volcanoes, and other hazardous areas can easily be generated using?
Buffer operations include:
a. variable width buffers
b. multiple ring buffers
c. doughnut buffer
d. setback buffer
e. non-dissolved buffer
f. dissolved buffer
the size of the buffer zone varies across points, line, segments, or polygon
Variable buffer
series of buffers is created based on the user-specified distance and buffering intervals
Multiple ring buffers
created when buffering a polygon but excludes the polygon itself resulting in an output buffer polygon with a whole inside
Doughnut Buffer
created when buffering only the inside part of the polygon
Setback buffer
does not dissolve overlapping boundaries between buffers while in the dissolved buffer the overlapping boundaries are dissolved
Non-dissolved buffer
- method that determines whether a point is inside or outside a polygon
- used to count the total number of points within a particular polygon
Points in polygon
user can determine if there are (how many houses) is located within a particular danger zone/s
Points in polygon analysis
method for determining overlaps between two polygons
Polygon overlay
commonly overlayed to produce an output polygon that contains the combination of attributes of the two inputs polygon
Two polygons
In hsp, it can be used to determine the proportion or the area of a particular settlement is under a particular zone
Polygon overlay
used to generalize geographic attributes or phenomena to reduce data complexity
measure of central tendency and dispersion
For attribute data, descriptive summaries such as the following can be calculated
- minimum
- maximum
- mean
- median
- mode
- standard deviation
- variance
- coefficient of variation
for locational data (geographic coordinates) the following can be calculated used as descriptive statistics
- centroid
- standard distance
- standard deviational ellipse
measure the average location of a geographic phenomenon
centroid or mean center
measures how the geographic phenomenon is concentrated or dispersed with respect to the mean center
Standard distance
calculated using equation centroid
x= summation n | n=1 xi
y= summation n| n=1 yi
*refer to equation 1.4.1
measures how the geographic phenomenon is concentrated or dispersed with respect to the mean center
standard distance
measure the directional trend of the geographic phenomena
standard deviational ellipse
standard distance is calculated using
equation 1.4.2 (descriptive summaries)
concerned with finding the best value or solution
Optimization or spatial optimization
uses mathematical and computation methods in order to find the best solution to a geographic decision problem given well-defined conditions
optimization or spatial optimization
process of minimizing or maximizing an objective with respect to a given problem with geographic nature (spatial sampling, route selection, location-allocation, land-use allocation)
optimization or spatial optimization
can be used to find the optimal location for a single or multiple facility (health centers) with respect to the spatial distribution of demands (households)
Location-allocation analysis
how to solve optimization problems?
requires formulation of an objective function subject to a set of constraints
Optimization problems can either be
- single objective problem
- multi-objective problem
Finding the optimal route from settlement to the nearest healthcare facility requires what function?
Single-objective function: minimize travel time
Finding the optimal route from settlement to the nearest healthcare facility
*For location-allocation of healthcare facility requires what function?
Two objective functions:
1. minimization of travel time from the settlement area to the healthcare facility
2. maximizing the number of households or settlements being served
Methods that can be used to solve spatial optimization problems which can range from
- linear programming
- integer programming
- heuristic approaches
provides exact solution but requires large computational time and power
Linear and integer programming
provide solutions that can either be optional or not for a shorter period of time
heuristic approaches
Heuristic approach used in spatial optimization problems include:
- naive or random strategy
- total enumeration
- greedy algorithm
- simulated annealing
- tabu search
- metaheuristic
- genetic algorithm
The use of standard statistical tests for hypothesis testing can be problematic when?
used in geographic or spatial data
(example) assumption of independence in samples is commonly not true in where?
geographic data due to spatial dependence
degree of spatial autocorrelation between independently observed or measured values in geographic space
Spatial dependence
Observed spatial autocorrelation illustrates?
Tobler’s First law of geography
- everything is related to everything else, but near things are more related than distant things”
spatial dependence of geogrpahic data can be tested using?
global and local measures of spatial autocorrelation
commonly used global measure of spatial autocorrection
- Moran’s I
- Getis-G
commonly used as local indicators of spatial association (LISA)
Local Moran’s I
defined as the presence of systematic variation in measured values in geographic space
Spatial Autocorrelation
Detecting the presence of spatial autocorrelation is important because
- indicator of something interesting in the distribution of geographic values that requires further investigation in order to understand the underlying cause of that particular distribution
- implies information redundancy which can affect the selection of methodology of spatial data analysis
accdg to Haining (2001) this may arise from any of the following situations
Spatial autocorrelation
accdg to Haining (2001), Spatial autocorrelation may arise from any of the following situations
- measurement error
- spatial interaction, dispersal, diffusion, and spill-over processes
- model misspecification
- inheritance by one variable through causal association with another
- difference in representing large and small scale variation of geographic phenomenon
the presence or absence of spatial autocorrelation directly influences the ff:
- spatial sampling
- spatial interpolation
- spatial data analysis
- spatial modeling
- statistical inference in spatial data
Accdg to Anselin (1995), it is any statistics that is an indicator of the extent of significant spatial clusters of similar values around a particular observation
local indicators of spatial association (LISA)
several LISA statistics
- Local Moran
- Local Geary
- Local Getis
- Local Getis-Ord
- Local Joint Count
- Local Quantile LISA
- Local Neighbor Match test
