Gis Final

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spatial data infrastructure

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-a spatial data infrastructure consists of a comprehensive and coordinated environment for the production, management, dissemination, and use of geospatial data, involving the totality of the relevant policies, technologies, data, institutions & individuals
-technology, policy,s tandards, human resources, and related activities necessary to acquire, process, distribute, use, maintain, and preserve spatial data
-a coordinated series of agreements on technology standards, institutional arrangements, and policies that enable the discovery and use of geospatial information by users and for purposes other than those it was created for

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problems that led to the establishment of SDI's

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-duplication of data
-inconsistent data collection methods (data non transferrable)
-no institutional relations (don't/won't share)
-no distribution mechanisms
-approach was ineffective/inefficient
-wasting money
-not meeting public access mandates
-poor documentation (no metadata)

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us nsdi

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-1994 executive order..national spatial data infrastructure:
-national geospatial data clearing house
-data standards (documentation, collection, exchange)
-policies, procedures, and partnerships

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INSPIRE designed to reduce the following barriers

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designed to reduce the following barriers:
-inconsistencies in spatial data collection
-lack of documentation
-incompatible spatial datasets
-incompatible geographic information initiatives
-barriers to data sharing

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INSPIRE aims to overcome barriers by:

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-creation of metadata
-harmonizing key spatial data themes to support policy
-forming agreements on network services
-makng policy agreements on sharing and access
-devising coordination and monitoring mechanisms
-creating the implementation process and procedures

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geodatabase

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top level unit of geographic data organization and is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS
~native data structure for ArcGIS used for editing & data management
~Access and work with datasets either through ArcGIS or through a database management system using SQL
~inherent modeling structure for representing and managing geographic info, this consists of a series feature classes, raster datasets, and attributes tables, rules for managing spatial integrity; and tools for working with numerous spatial relationships

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personal geodatabases

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all datasets are stored within a microsoft access data file, which is limited in size to 2GB. the original type of geodatabase, but now relatively limited in scale, efficiency and user accessibility

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file geodatabses

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stored as folders in a native file system
-each dataset is held as a file that can scale up to 1TB in size. provides several advantages over personal geodatabases, and is the recommended option for single users and small workgroups

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arcSDE geodatabases

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stored in a relational database using oracle, microsoft sql server, ibm db2, ibm informix, or postgresql. these multi user geodatabases require the use of arcsde and can be unlimited in size and numbers of users

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key geodatabase elements

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-tables: a collection of rows, each containing the same fields. feature classes are tables with shape fields (dbase files, microsoft access tables, excels spreadsheets, dmbs's)
-feature classes: a table with a shape field containing point, line, or polygon geometries for geographic features. each row is a feature (shapefile)
-raster datasets: contains rasters which represent continuous geographic phenomena (imagery)

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hierarchy of geodatabase elements

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-feature dataset (feature class: point, line, polygon, annotation)
-table (relationship class, subtype)
-raster catalog or mosaic (raster dataset)
-network dataset

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tables

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-tables contain rows
-all rows in a table have the same columns
-each column has a data type, such as integer, decimal number, character, date
-a series of relationship functions and operators (such as SQL) is available to operate on the tables and their data elements

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extended capabilities of tables: attribute domains

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specify a list of valid values or a range of valid values for attribute columns. use domains to help ensure the integrity of attribute values. domains are often used to enforce data classifications (such as road class, zoning codes, and land-use classificications)

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extended capabilities of tables: relationship classes

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build relationships between two tables using a common key. find the related rows in a second table based on rows selected in the original

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extended capabilities of tables: subtypes

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manage a set of attribute subclasses in a single table. this is often used on feature class tables to manage different behaviors on subsets of the same feature types

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extended capabilities of tables: versioning

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versioning allows multiple users to edit the same data in an arcsde geodatabase

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feature class

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point, line, polygon, annotation

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raster data organization

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~raster datasets: manage very large, continuous image datasets
~mosaic datasets: they allow you to store, manage, view, and query collections of raster image data
~raster attribute columns in tables: store pictures or scanned documents as attributes in tables

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nature of geog. data

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-representations are selective
-some geographic data vary smoothly across space, while others can exhibit extreme irregularity (tobler's law)
-spatial heterogeneity (measure of this: spatial autocorrelation, which is the correlation between values of the same variable at different spatial locations)

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spatial autocorrelation

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-data from locations near one another in space are more likely to be similar than data from locations remote from one another
-looks at neighbors:
~+ spatial autocorrelation:
attributes that are near each other in space are similar
~-spatial autocorrelation: attributes that are near each other in space are not similar

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z-score and p-value

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z=standard deviation
p=probability

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spatial sampling

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sample designs: random sampling (pure bad luck can produce a bad sample)
-spatially systematic sampling schemes can improve this (stratified, cluster, contour, transect)

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distance decay

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-you have points, but how do you fill in the space between them?
-choice of distance decay measures: past experience, fit to a dataste, convention
~linear distance decay (noise levels, important around airports)
~negative power distance decay (resident populations from a historic business district)

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negative exponential decay

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-represents the decrease in retail store patronage with distance

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isotropic

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uniform in every direction

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situations in which a scientist may want to measure spatial autocorrelation

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-point data (wells with attributes store in a spreadsheet: linear extent of image .6km)
-line data (accident rates in the southwester Ontario provincial highway network)

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spatial analysis

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all of the transformationa, manipulations, and methods that can be applied to geographic data to: add value, support decisions, and reveal patterns and anomolies that are not inherently obvious (process of turning raw data into useful info, in pursuit of scientific discovery, or more effective decision making)

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point in polygon

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-point inside or outside a polygon
-the point in polygon problem, shown in the continuous field case
-the point must by definition lie in exactly one polygon or outside the project area

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intersect

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-intersect method preserves only those features falling within the spatial extent common to both layers
-the features of the input layer are intersected or sliced by the intersect layer. the attribute data from both layers are included in the new layer's attribute table

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union

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-union creates a new layer by combining two polygon layers. the new layer has data and shapes from both layers, including their intersection...differs from intersect only by the fact that all the features of both layers are included in the resultant layer, including those features that did not overlap

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geoprocessing

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spatial data integration: involves using data from one or more spatial layers and integrating it to form a new, single layer

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clipping

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creates a new layer by using a polygon layer (or selected polygons from a particular layer) as a cookie cutter on a point, line, or polygon shapefile. the output layer contains info from layer A only. layer B is only used to define the new boundary

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dissolve

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dissolving features in a layer coalesces features that have the same attribute value. this tool is extremely important if you are trying to create a new shapefile, a file with a coarser layer of geography than your starting files

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merge

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similar to union; a new layer is created from multiple layers but their features are not intersected. merge allows you to combine the features from two or more layers of the same geometric type

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distance in arcmap (vector)

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-vector line segments (automatic)
-measure tool (basic)
-spatial tool - closest feature

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distance in arcmap (raster)

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-euclidean (straight line) distance
-cost weighted distance (cost weighted distance allowing for vertical and horizontal restrictions to movement...paths and corridors between sources with the least cost of travel)

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euclidean distance

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gives the distance from each cell in the raster to the closest source (what is the distance to the closest town?)

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euclidean direction

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gives the direction from each cell to the closest source (what is the direction of the closest town)

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euclidean allocation

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identifies the cells that are to be allocated to a source based on closest proximity (what is the closest town?)

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cluster detection

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-clustered: come locations are more likely than others, and the presence of one point may attract others to this location
-dispersed: the presence of one point may make others less likely in its viciinity
-random: points are located independently, and all locations are just as likely

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what causes clustering?

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-first order mechanisms: involve points being located independently
-second order mechanisms: involve interaction between points

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examining clustering in arcmap: avg nearest neighbor

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calculates a nearest neighbor index based on the average distance from each Feature to its nearest neighboring feature

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examining clustering in arcmap: high/low clustering (getis-ord general g)

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measures the degree of clustering for either high values or low values using the Getis-Ord general G statistic. results are accessible from the results window

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examining clustering in arcmap: spatial autocorrelation

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looks at neighbors

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spatial autocorrelation moran's I

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measures spatial autocorrelation based on feautre locations and attribute values using the global moran i's statistic

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multi-distance clustering - ripley's k

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-use this tool when you are interested in examining how the clutering/dispersion of your features changes at different distances or different scales of analysis

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kernel density

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calculates a magnitude per unit area from point or polyline features using a kernel function to fit a smoothly tapered surface to each point or polyline
-most often applied to an estimation of point density (kernel is run over a series of points)
~points are replaced by its kernal function
~various kernel functions are added to obtain a surface
~magnitude per unit area
~size
~shape

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line density

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calculates a magnitude per unit area from polyline features that fall within a radius around each cell

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point density

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calculates a magnitude per unit area from point features that fall wtihin a neighborhood around each cell

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spatial interpolation: pervasive operation

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-procedure to predict values of attributes at unsampled points: GIS version of intelligent guesswork
-three methods: continuous field data measured at points (thiessen polygons, inverse distance weightin, kriging)

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thiessen polygons

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simple method of interpolation
~mathematically defined by the perpendicular bisectors between all points
-created from point data
+ease of application,
-accuracy depends largely on sampling density, continuous variables often not well represented

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inverse distance weighting

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-workhorse of spatial interpolation: values determined as the weighted average of nearby points
-weights usually calculated using the inverse distance
-interpolated value is an average over the observed values
-Tobler's law: everything is related to everything else, but near things are more related than distance things
-common in GIS

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moran's I v. getis ord general G

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moran's I only tells you that your data is clustered, dispersed or random (pattern detector)
-general g tells you the type of cluster that exists...measures the concentration of a parameter (for example identifying the location where are the largest number of calls made by people with the flux)
-both tools are global statistics...the null hypothesis is that the values being analyzed exhibit no spatial pattern (they are random)...if the z score is not significant, you cannot reject this null hypothesis and must conclude the spatial distribution of your values could have been generated by some underlying spatial processes
-moran's I: positive z score means that similar values cluster spatially (high values found closer together and low values found closer together)...negative z score means that similar values are spatially dispersed...dispersion is less common but might be seen with some type of competitive or territorial spatial process, where similar features try to be as far away from each other as possi

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kriging

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geostatistical method that focuses on statistical relationships among the measured point
-assumes the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface

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centroid

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mean center replicates the balance-point property in two dimensions - the point about which the two dimensional pattern would balance if it were transferred to a weightless, rigid plane and suspended

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aspect

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slope direction

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hillshade

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determining the hypothetical illumination of a surface by determining illumination values for each cell in a raster

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contour

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create lines from a raster surface
-connect locations of equal value

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slope

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calculates the max rate of change in value from that cell to its neighbors

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hypothesis testing

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using and drawing inferences using statistics
-inferential: uses info from a sample to make a general conclusion about a larger population
-problems with geog data: can i conceieve a larger population about which i want to make inferences?
-are my data acceptable as random and independent sample of that population?

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tangible benefits of gis

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-revenue growth and protection
-health and safety
-cost reduction and avoidance
-increase efficiency and productivity

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intangible benefits of gis

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-increase regulatory compliance
-improve effectiveness
-add new capabilities
-improve image
-enhance customer satisfaction
-improve staff wellbeing