ESPM 72 Midterm 2

Problems from incomplete representations of world often due to no natural units of geog analysis, uncertainty can be in positions of boundaries and in attributes of a zone, more pronounced with raster data, simulations can calculate full distribution of possible outcomes to help avoid extreme events and make decision making about uncertainty easierFuzz

Much ambiguity exists in terms to convey geog info and labels assigned by diff cultural groups (eg NLDC classifications), GIS can provide framework to reconcile worldviews

Assign values based on idea that probability of outcome = proportion of ties the outcome occurs in experiment when # tests sufficiently largeData

Specialist software to handle multi-user access to an integrated set of data; organizes storage and access of data, forms basis all queries/analysis/decision making - understand = better at GIS

Pros: reduces redundancy/allows multiple applications/transfer of knowledge + data sharing/security and standards/concurrent users, Cons: cost/complexity/single user performance decreased

Data model = mechanism rep real world, Data load capability = can continuously add data, Indices = speeds up quarries, Query language = typically SQL, Security = not everyone has access, Controlled update = updates on multiple parts of database are coordinated, Backup and recovery = protection from system failure, Database admin tools = backup/update/recover data, Apps used to make more user friendly

Set of tables with 2D list of records w/ attributes of study object

Designed to counteract inability of DBMSs to store complete objects directly in database (object state and behavior) but has poor performance for geog queries

Includes geog query parser/optimizer/language, multidimensional indexing services, storage mgmt for large files

Object/layer/feature class is lowest level of user interaction in gdb which are stored in standard database table w/ rows = objects and columns = attributes

only one value in each cell, all values inc olumn are same subject, each row is unique, no significance to sequence of columns/rows

Language: allows select what columns from which tables to use, use from to identify any manipulations of tables (joins), and where to set constraints

Determine objects definitely in study area or definitely out

“Data about data”, used to automate processing of archives/assess hot good dataset is for given use/provide info to handle dataset & tell about its contents, requires pro expertise so expensive and metadata can be more voluminous than actual data

location of User U, data D, where data is processed P, GIS subject S, where location of User does not equal location of subject

User location does equal location of subject, extends awareness of what’s happening at a location, can either be traditional or wireless (both collect data in field, trad stores temporarily on unit and office upload vs wireless upload to server then just analysis later)

easily collect locational/attribute info, mobile devices also have camera/video/voice recording, paperless workflows, dissemination via smartphones, free access to maps/location based services

not enough storage for large databases, limited analysis computing power on devices, screens are small (but resolution great)

Identify where user is and what’s around them, location (gps/cell tower) to receiver (phone/gps device) to application server (google/esri), offer Pull services (user requests info from service provider) and Push services (provider pushes info when user near location w/ a service)

Providers must ensure users now how location will be used/disclosed/protected and attain revokable consent, can protect privacy vs anonymity/obfucation (downgrading quality of info about someone’s location)/deleting data periodically

distributed GIS, involves high speed networks/massive processors/distributed network of sensors + archives w/ real time feed to web, allow collaboration collection geospatial info/mapping/visualization/query

Static maps are paper maps on web (can’t change data), Animated web maps have data animated not interactive, realtime web maps have continually updated data, interactive maps, analytic webmaps

No download/store/saving data, can use multiple servers and not have to own/maintain them, use from any device + cheap

Statement of how the world looks, Vector vs Raster; framework to fit specific details of Earth’s surface

Statement of how the world works, expressions of processes, can incorporate underlying data models, supported by computers

Variation across space, result of model change when locations of objects change, spatial resolution typically regarded as smallest unit over which change in model occurs, temporal resolution is smallest time frame over which a change occurs (resolutions determine what’s left out of model/level uncertainty/cost acquiring data + running model)

To understand processes and predict outcomes under alternative scenarios

Each raster cell has # possible states which change through time depending on rules defined in cell neighborhood (based on cell’s state/state of neighbors/values of cell attributes)

1) Live cell with <2 live neighbors dies (underpopulation), 2) Live cell with 2-3 live neighbors lives, 3) Live cell with >3 live neighbors dies (overcrowding), 4) Dead cell with 3 neighbors resurrects (reproduction)

Pixels are individual actors that typically follow a set of rules for changes in state/position, rules are set by expert knowledge/stat analysis

Each agent belongs to one of two groups (equal size) and tries to be in a neighborhood where fraction of “friends“ is above tolerance threshold value F, depending on F the pattern will converge to either complete integration (random) or segregation

Use regressions to model spatial processes which can then predict future occurrences, spatial weights matric can model relationships, formula represents the direct impacts per coefficients of independent on dependent variables, also include indirect impacts (spatial lag) where coefficients also show impact of each dep/indep vars on their neighboring dep/indep vars

Cellular = when interested in spatial impacts of nearby/adjacent cells, Agent Based = when interested in processes at scale of the individual, Statistical = when interested in processes driving spatial actions at multiple scales/predicting changes in outcomes when drivers change

Cellular = low (just enough rules to create patterns of interest), Agent Based = low (schelling) to high, Statistical = high (need all variables that impact process of study)