GIS questions

What does the "First Law of Geography" state?

"All things are related, but nearby things are more related than distant things".

Describe the major approaches towards "surface smoothing". These involve (1) resolution increase (2) grid recalculation and (3) filtering/kernel smoothing

(i) resolution increase (this may be the main objective of the procedure) Smoothing a grid by resolution increase involves increasing the number of nodes or cells and then fitting an exact smoothing interpolator through these points and the original set. Typically the function used will be a bicubic spline. (ii) grid recalculation, which if the grid has fewer rows and columns than the original is sometimes described as thinning. Smoothing a grid by recalculation involves defining the number of grid rows and columns required, and then computing the result by interpolation to this resolution whilst again honoring the original point set. (iii) filtering or kernel smoothing, involving operations on NxN windows or kernels.

BOOLEAN operators A AND B

(same as intersect)

BOOLEAN operators A OR B

(same as union)

Kriging interpolation

A method that mathematically estimates a surface that fits a certain number of points. Uses a predictive model, is probabilistic because it is based on the semivariogram. Semivariograms measure the strength of statistical correlation as a function of distance; they quantify spatial autocorrelation. Kriging equations are determined by fitting line throgh points so as to minimize weighted sum of squares between points and line. These equations are weighted based on spatial autocorrelation, which isa determined from the semivariograms Suitable when you know the data has high spatial autocorrelation, eg. soils For ordinary kriging data: - must be normally distributed - must be stationary (no local effects) - must be trend free, does not support data with trends

GNSS

A system to calculate a position or coordinate on the earth surface Composed of 3 segments Space segment (satellites) Control segment (ground facilities that track the GPS satellites, monitor their transmissions, perform analysis and send commands and data to the constellation) User segment (GPS RECIVERS, tex a GPS data logger, gps receiver for hiking) Three spheres to locate the position on earth

Inverse distance weighted IDW

A type of Spatial moving average Medelvärde på alla punkter inom varje pixel. Punkter nära pixelns mitt får större vikt. Weight of each sample point is an inverse proportion to the distance. Power determines the relative influence further away. Lower power increases influence from points a a greater distance from given pixel. Bra för miljö, ekologiska data, kontinuerliga-föroreningar, LAI Approximate

Vad är den största skillnaden mellan BOOLSKA operatörer, logiska operatörer samt matematiska operatörer

BOOLSKA operatörer: (om ett lager ska vara lika eller olika (binär) Matematiska operatörer: "+", "-" eller "x" till exempel. Om lager ska vara flera saker, bra för diskreta data. Bra för att bygga index (värde) Logiska operatörer: ">" eller "<" eller "<=" till exempel. Om lager ska uppfylla ett krav (bra för att göra kontinuerliga data diskreta). Du får ett binärt lager

Thiessen polygons (voronoi polygons)

Basically polygons are created that represent the area closest to one data point. Assumes that the values of unsampled locations are equal to the value of the nearest sample point. Thiessen Polygons are created by sub dividing lines joining nearest neighbor points, drawing perpendicular bisectors through these lines and then using the bisectors to assemble the polygon edges. Bra för kategoriska data (ex jordart) Local, abrupt Ian: Only creates confidence within a border pattern of our point density affects the shapes of our polygons

Spatial moving average

Calculates a value for a location based on the range of values attached to the neighboring points that fall within a user defined range. Suppresses the values of known data points to reveal global patterns in the data. Best suited where the values of the known data points are not exact and may be subjected to measurement error, men fortfarande visar en variation I globala mönster

How do you calculate the density of something in an area?

Density provides an effective link between the discrete-object and continuous-field conceptualizations, since density expresses the number of discrete objects per unit of area, and is itself a continuous field. Mathematically, the density of some kind of object is calculated by counting the number of such objects in an area, and dividing by the size of the area.

Dangles, switchbacks, knots, loops, overshoots, undershoots and slivers

Describe the following digitizing errors

File geodatabases

File geodatabases are a convenient and efficient way to organise project data. You can easily share and transfer data in a file geodatabase. Good when working on projects with a lot of different people involved. A file geodatabase can easily be shared between different departments in a project, where everyone in the project gets access to the files uploaded from the different departments to the file geodatabase, without having to sit by or access the same computer. !!!Offers the ability to check your topology!!! It is basicly like a shared drive

What is the difference between the "Forward" and "inverse" model?

Forward method - Model parameters -> Observed data Inverse model - Observed data -> Model parameters Inverse models commonly ill-posed and sensitive to overfitting

What is the major difference between a Geographic coordinate system and a Projected coordinate system?

Geographic coordinate system A geographic coordinate system (GCS) uses a three-dimensional spherical surface to define locations on the earth. A point is referenced by its longitude and latitude values. Longitude and latitude are angles measured from the earth's center to a point on the earth's surface. Projected coordinate system A projected coordinate system is defined on a flat, two-dimensional surface. A projected coordinate system is always based on a geographic coordinate system that is based on a sphere or spheroid. Unlike a geographic coordinate system, a projected coordinate system has constant lengths, angles, and areas across the two dimensions

What is a: a) geoid b) Spheroid c) Ellipsoid d) datum

Geoid The geoid is the shape that the surface of the oceans would take under the influence of Earth's gravity and rotation alone, in the absence of other influences such as winds and tides. The geoid is a model of global mean sea level that is used to measure precise surface elevations. Spheroid/Ellipsoid An earth ellipsoid is a mathematical figure approximating the shape of the Earth To simplify the model of the geoid, various spheroids or ellipsoids have been devised. These terms are used interchangeably. A spheroid is a three-dimensional shape created from a two-dimensional ellipse. The ellipse is an oval, with a major axis (the longer axis), and a minor axis (the shorter axis). Various different ellipsoids have been used as approximations. Datum A datum is built on top of the selected spheroid, and can incorporate local variations in elevation. With the spheroid, the rotation of the ellipse creates a totally smooth surface across the world. Since this doesn't reflect reality very well, a local datum permits local variations in elevation to be incorporated. While a spheroid approximates the shape of the earth, a datum defines the position of the spheroid relative to the center of the earth. A datum provides a frame of reference for measuring locations on the surface of the earth. It defines the origin and orientation of latitude and longitude lines.

What is a Geostatistical (same as probabalistic/stochastic?) interpolation method? How does it differ from determintic interpolation methods?

Geostatistical techniques create surfaces incorporating the statistical properties of the measured data. Produces not only prediction of surfaces, but uncertainty estimates of prediction. Deterministic: all data is known beforehand, Probabilistic (stochastic?): there is a possibility of chance in the analysis. There are two main groupings of interpolation techniques: deterministic and geostatistical. Deterministic interpolation techniques create surfaces from measured points, based on either the extent of similarity (inverse distance weighted) or the degree of smoothing (radial basis functions). Geostatistical interpolation techniques (kriging) utilize the statistical properties of the measured points. Geostatistical techniques quantify the spatial autocorrelation among measured points and account for the spatial configuration of the sample points around the prediction location. Deterministic interpolation techniques can be divided into two groups, global and local. Global techniques calculate predictions using the entire dataset. Local techniques calculate predictions from the measured points within neighborhoods, which are smaller spatial areas within the larger study area. Geostatistical Analyst provides global polynomial as a global interpolator and inverse distance weighted, local polynomial, radial basis functions, kernel smoothing, and diffusion kernel as local interpolators. A deterministic interpolation can either force the resulting surface to pass through the data values or not. An interpolation technique that predicts a value that is identical to the measured value at a sampled location is known as an exact interpolator. An inexact interpolator predicts a value that is different from the measured value. The latter can be used to avoid sharp peaks or troughs in the output surface. Inverse distance weighted and radial basis functions are exact interpolators, while global polynomial, local polynomial, kernel interpolation with barriers, and diffusion interpolation with barriers are inexact. There are two main groupings of interpolation techniques: deterministic and geostatistical. Deterministic interpolation techniques create surfaces from measured points, based on either the extent of similarity (inverse distance weighted) or the degree of smoothing (radial basis functions). Geostatistical interpolation techniques (kriging) utilize the statistical properties of the measured points. Geostatistical techniques quantify the spatial autocorrelation among measured points and account for the spatial configuration of the sample points around the prediction location. Deterministic interpolation techniques can be divided into two groups, global and local. Global techniques calculate predictions using the entire dataset. Local techniques calculate predictions from the measured points within neighborhoods, which are smaller spatial areas within the larger study area. Geostatistical Analyst provides global polynomial as a global interpolator and inverse distance weighted, local polynomial, radial basis functions, kernel smoothing, and diffusion kernel as local interpolators. A deterministic interpolation can either force the resulting surface to pass through the data values or not. An interpolation technique that predicts a value that is identical to the measured value at a sampled location is known as an exact interpolator. An inexact interpolator predicts a value that is different from the measured value. The latter can be used to avoid sharp peaks or troughs in the output surface. Inverse distance weighted and radial basis functions are exact interpolators, while global polynomial, local polynomial, kernel interpolation with barriers, and diffusion interpolation with barriers are inexact. Geostatistical Analyst derives a surface using the values from the measured locations to predict values for each location in the landscape. Geostatistical Analyst provides two groups of interpolation techniques: deterministic and geostatistical. All methods rely on the similarity of nearby sample points to create the surface. Deterministic techniques use mathematical functions for interpolation. Geostatistics relies on both statistical and mathematical methods, which can be used to create surfaces and assess the uncertainty of the predictions.

What is a topology?

In mathematics, a property is said to be topological if it survives stretching and distorting of space. Topological properties are those that cannot be destroyed by stretching or distorting the space. Topological: Dimensionality Adjacency Connectivity Containment

Describe the different ways in which you can meassure distance in a raster, and also how the work

Pythagorean distance Manhattan distance Proximity Distance Perimeter & Area Chessmoves and sqrt of 2 (1.414...)

What is the definition for a convolution mathematicaly?

Mathematically, a convolution is a weighted average of a point's neighborhood, the weights decreasing with distance from the point, and bears a strong technical relationship to density estimation. As long as the weights are positive, the resulting pattern will be smoother than the inputs. The blurring of an out-of-focus image is a form of convolution.

Describe the major resample methods (interpolation from on point to another point) E.g. interpolation to convert from one level of data resolution or orientation to another (resampling).

Nearest neighbor: closest cell Best for discrete data, eg landuse. Preserves data values, inga nya värden skapas. Billinear interpolation: distance weighted average with 4 nearest points best for continuous data eg elevation. Does not preserve data values, nya värden skapas Cubic convolution: distance weighted average with 16 points best for continuous data eg elevation. Does not preserve data values, nya värden skapas. More processing time compared to bilinear. Tends to sharpen edges

What is the definition for "Networks"?

Networks constitute one-dimensional structures embedded in two or three dimensions

Skalor/värden som attributen kan delas in i är...? (5 categories, NOIRC)

Nominal An attribute is nominal if it successfully distinguishes between locations, but without any implied ranking or potential for arithmetic. Ex telefonnummer, objectid, nummer för olika landklasser, fruktnamn. Ordnial Ordered. An attribute is ordinal if it implies a ranking Interval Quantitative. Attributes are interval if differences make sense, as they do for example with measurements of temperature on the Celsius or Fahrenheit scales, or for measurements of elevation above sea level Ratio Quantitative. Attributes are ratio if it makes sense to divide one measurement by another. Negative values cannot exist on a ratio scale. Cyclic Quantitative. Directional data are cyclic, as are calendar dates. Arithmetic operations are problematic with cyclic data, and special techniques are needed.

Using a DEM, why would you use pit filling in your ananyses?

Pit filling Grid models of terrains often contain individual cells or groups of cells that are surrounded by cells with larger (higher) values. Pits are uncommon features of natural terrains, except in karst landscapes and some types of desert, so in many instances observed pits arise from errors in data capture and subsequent modeling of the surface. Pit filling is thus primarily of form of error correction procedure.

Fördelen med en file geodatabase

Sharing is caring In geodatabases, topology is the arrangement that defines how point, line, and polygon features share coincident geometry. topology rules can be applied. Topology is a collection of rules that, coupled with a set of editing tools and techniques, enables the geodatabase to more accurately model geometric relationships

Fill in the blank words The term gradient refers to a vector quantity, i.e. an object that has both magnitude and direction. The magnitude or size of the gradient is the ____, whilst the direction in which the maximum value of this magnitude occurs is known as the ____.

Slope and aspect The term gradient refers to a vector quantity, i.e. an object that has both magnitude and direction. The magnitude or size of the gradient is the slope, whilst the direction in which the maximum value of this magnitude occurs is known as the aspect. Slope calculations are extremely sensitive to scale. Larger grid intervals will result in smoothing of slope values. Slope computation is also affected to a greater or lesser degree by grid orientation, since it relies on grid values in particular directions. Aspect is defined as the directional component of the gradient vector and is the direction of maximum gradient of the surface at a given point.

what is "Spatially extensive attributes"?

Spatially extensive attributes include total population, measures of a place's area or perimeter length, and total income — they are true only of the place as a whole.

What are "Spatially intensive attributes"?

Spatially intensive attributes include population density, average income, and percent unemployed, and if the place is homogeneous they will be true of any part of the place as well as of the whole.

Spline interpolation

Spline Statistical method, fits curve through the dataset Regularized and tension Tension results in a rougher surface that more closley adheres to abrupt changes in sample points Regularized results in a smoother surface that smoothes out abruptly changing values somewhat

Error modelling - Epsilon model - Monte Carlo model

The Monte Carlo model is a risk and uncertainty estimation simulation of a forecasting model

What is the coastline paradox?

The coastline paradox is the counterintuitive observation that the coastline of a landmass does not have a well-defined length. This results from the fractal-like properties of coastlines. The first recorded observation of this phenomenon was by Lewis Fry Richardson and it was expanded by Benoit Mandelbrot.

MUAP

The modifiable areal unit problem (MAUP) is a source of statistical bias that can significantly impact the results of statistical hypothesis tests. MAUP affects results when point-based measures of spatial phenomena are aggregated into districts, for example, population density or illness rates. The resulting summary values (e.g., totals, rates, proportions, densities) are influenced by both the shape and scale of the aggregation unit. Two aspects: Scale effects Zonation effects

There are 3 data models (raster, vector and attributes) describe the structure of these Also describe the pros and cons of vector vs raster

Tre data modeller Raster (spatial data): matrix of square cells, contains single values +kontinuerlig data -ser kantigt och fult ut -hög upplösning kräver mkt datatrafik -Very affected by spatial resolution as opposed to vector Vector: (spatial data) points, lines, polygons +diskreta data +flera attribute per object +snyggt att visualisera +not so affected by spatial resolution as raster +vectors offer a more efficient storage due to encoding scheme (as a vector is essentially a data value, coordinates and metadata) -Vector data typically requires more computer processing compared to raster Attributes: (non spatial data) descriptive information, part of vector data Conversions from one datamodell to another can alter the features true location, shape, area and therefore increase the data uncertainty. Affected by raster resolution. Convert only if absolutely necessary.

Triangulated irregular network TIN

Triangulära polygoner skapas mellan intilliggande punkter. Polygonen får medelvärdet av de tre punkterna. In this method adjacent data points are connected by lines to form a network of irregular triangles. Because the value of the points is known and the distance can be calculated, a linear equation and trigonometry can be used to work out an interpolated value for any other point within the boundary of the TIN. It's creates a continuous surface as opposed to the Thiessen polygon method. However, because the TIN-method relies on interpolating values between neighboring data points it is impossible to extrapolate outside the data points making up the convex hull or boundary of the sample points. Bra (endast) för höjddata Local, exakt Ian: Popular for high precision studies

Name and describe the 4 V´s of big data

Volume Quantity of data Traditionally created manually Today, mostly generated automatically Dataset sizes up to the range of Peta-/Exabytes More and more data is freely accessible Larger scale analysis increases complexity Processing power requirements increase rapidly Variety Increasing diversity in data sources/types Benefits multi-/transdisciplinary research Complicates storage and accessibility Requires new definitions for hierarchy Absurd number of different types of data available for analysis Velocity Speed at which data is generated Massive and continuous data flow Sampling the data a solution for velocity issues Acquisition rate increasing Older data becoming more freely available constitutes a massive increase in velocity Veracity Refers to data quality Noise, bias and accuracy issues pollute large datasets

Trend surface

attempts to fit a mathematically defined surface through all the data points so that the difference between the interpolated value at a data point and its original value I minimized Approximate, global

Classifications of interpolation methods, what is the difference between: local or global? exact or approximate? gradual or abrupt? deterministic or stochastic?

classification of interpolation methods Local or global Global interpolators determine a single function which is mapped across the whole region Local interpolators apply an algorithm repeatedly to a small portion of the total set of points Exact or approximate Exact interpolators honor all data points Approximate interpolators try to approach all data points Gradual or abrupt Gradual interpolators assume continuous and smooth behavior of data everywhere Abrupt interpolators allow for sudden changes in data due to boundaries or undefined derivatives. Deterministic or stochastic Deterministic interpolators model a data point at a particular position. Stochastic interpolators try to model probability of a data point being at a particular position (e.g. kriging, fourier analysis)

The interpolation models used may be "exact" or "inexact". What are the differences between these?

exact (go precisely through the sample data points) inexact (approximate the values at the data points)

Accuracy

is the extent to which an estimated data values approaches its true value

Precission

is the recorded level of detail of your data