geog 461 midterm 1 review

• •A bit of an artificial separation
• •Physical sciences / processes – raster?
• •SocSci / human processes – vector?
• •Not exactly, and besides urban processes intersect both.
• •But still, there are some unique issues / challenges to urban GIS

• •Census
• •Land records
• •Cadastral data
• •Property records
• •Utilities / infrastructure•Image data

• Researching title
• Assessing historical patterns in land administration, land use, etc.
• Public service delivery and record keeping (taxation, schooling, zoning, public health, etc).
• Emergency services

• 1.Approval by various gov‟t agencies
• 2.Record with Register of Deeds, County Recorder, etc.
• 3.Add identifying information for tax assessor
• PIN, address, map sheet #, etc.
• 4.Update all related hard copy and digital records in local gov‟t system

Dimensions

• Administrative classifications (ID, zoning, land use, etc)
• Taxable value, estimated resale value

Characteristics of structures on it (size, condition, type, address)

People who live there or are linked to it (i.e. registered taxpayer)

Ownership history, transfers, prices

Format (may not always be digital)

Availability (varies widely, as do rules/costs for obtaining)

• Maintenance (hard to keep current in large urban areas!)
• Quality (see above – highly variable)

Augmentation (adding our own additional information to parcel boundaries?)

Confidentiality (lots of potential issues!)

Condos & PINs

Separated rights (i.e. mining, transportation authorities, air or subsurface water rights)

Encumbrances (roads, utilities, easement between sidewalk and street)

Zoning vs. Land use

Re-platting & other changes

Associated with many types of features in real world

Associated with different spatial objects (with different geometries) in GIS

Relationships between addresses and real world phenomena may be one-to-one or many-to-one

• Addresses (might)
• Have sub-address
• Have zone
• Be assigned to a building/parcel (or more than one)
• Be assigned to a landmark

All of these associations describe relationships between different entities and attributes

Solution: Relational Databases!

House or building number

Street name

Street type

Directional component

• Zone information
• City, state, ZIP, postal code, province, etc.

Multiple cities may have same address

Sort/Index large data set

Search/Processing efficiency(sometimes) Used to geocode or join

Tip: Zones are a great place to set a domain range in an ArcGIS geodatabase!

To/from node

Or, in U.S. Census & TIGER files:

From Address Right

To Address Right

From Address Left

To Address Right

Descriptive location (i.e. address) -> exact geographic reference

• The process involves:
• Input (the location you are geocoding)
• Reference data set („base map‟)
• Processing algorithm (math / geometry)
• Output (usually an x/y pair, not always)

Normalizes & standardizes input

• Searches reference data set
• User-determined tolerances

Returns geocoded output OR user notification of failure to match

[If no match: adjust tolerance, try again]

• Tolerance is set too low/relaxed
• Input is matched to the wrong location

• Reference dataset is inaccurate
• Output is not in the correct „real world‟ location, even though it is correctly located on the map.

• Parcels not same size / address range is wrong
• Output is in the wrong place on the map & the assigned geog. Coordinate is not the „real world‟ location

What is your application? How will you use the output data set?

Are you combining output data set with other data?

What is the scale of your analysis and/or map?

Informal settlements

PO boxes & other „non standard‟ addresses

Multi-family dwellings (apts, condos)

Industrial / commercial areas

• TIGER/Line files (street centerlines)
• Full US coverage, free
• Lower accuracy, esp for local applications

• Street centerlines from municipal gov‟t
• More current than TIGER
• May not cover whole study area
• Metadata & accuracy vary widely

• Parcel address points
• Highly accurate, but not available everywhere
• Expensive

At first, addresses only.

• Now, ZIP codes, parcels, „natural language‟ descriptions
• But, parcels are less accurate / reliable!

• Why?
• More (& better) digital reference data sets
• More robust interpolation algorithms

But…declining gov‟t funding for development of reference data sets

• Spelling errors or typos
• May be in input or in reference layer

• Missing segments
• Your address not in existing ranges

• Street is named differently along a portion
• “Cicero” vs. “Hwy 50”

Try a „select by attribute‟ to highlight the entire street

Have a close look at your attribute table

Most everyday work of gov’t uses spatial data

• Practices of creating, sharing, using spatial data vary widely by:
• Scale of gov’t (local, regional, state, national, etc)
• National context

The social and political construction of data affects how we perceive the world to be, and how we try to act to change it.

Spatial data are not purely ‘technical’

The data themselves, as well as access, use, implications are determined by more than the just the technologies themselves

Data <-> Society

• Political contexts
• e.g. access to gov’t data, U.S. vs E.U.

• Cultural contexts
• Rules, beliefs, preferences, accepted behaviors
• e.g. norms about data privacy,

• Organizational / institutional contexts
• Budgets, methods, staffing, technologies, ‘political capital’
• GIS adoption in Milwaukee WI, vs. Cochise Cty, AZ

Context matters – type or size of organization, its attitude toward sharing, its national or regional situation

Social and political connectivity and relationships matter

Formal codes and rules matter, but so do informal rules, unwritten expectations, and individual quirks

Policies often have unexpected consequences

The human, technological, and informational resources used to manage and share large collections of spatial data

Includes data, metadata, networks, regulations, people, and organizations

SDIs have been the predominant model for governmental spatial data handling in the US

Most SDIs are national level – local SDIs have proved harder to implement

Compatibility between different data or computing environments that allow us to integrate or move between them.

• Data, systems, network
• In GIS, data interop is biggest challenge

Also called „semantic interoperability‟

• Difficult to achieve because of high levels of semantic heterogeneity
• „green‟, „green‟, „green‟.

Data collection methods

• Purposes of data collection
• Institutional differences

• Classification schemes
• Even the same classification scheme may be applied differently…

Commonly uses census data

Thematic mapping, spatial statistics

• Used for
• Defining political districts
• Allocating public funds
• Policy making / programming decisions

Questions asked, not asked

• Changing terminologies / definitions
• Race; „head of HH‟ vs „householder‟

• Change in how you can answer the Qs
• 2000 –multiple race identifiers accepted

• Enumeration units
• Watch out if you are doing historical analysis

• 2000 was first fully digital/GIS-able censuses (but only in some countries!)
• But early digital spatial data: DIME, 1970s

Devolution of gov‟t = devolution of data

• Growing concern about the data
• Mistrust, privacy
• Undercounts
• Concern about attributes and their definitions (i.e. who is a household?)

Data collection & aggregation

Census geographies

• Data organization / structure
• Tables & variables

• Short form (all)
• Race, Hispanic origin, age, sex, housing tenure, household relationships

• Long form (1 in 6)
• Income, language, work status, home values, telephone, etc etc…

• Short form (all)
• Race, Hispanic origin, age, sex, housing tenure, household relationships

• No more long form!
• American Community Survey to obtain detailed data

Original data aggregated at many different summary levels

• Governmental units
• “Seattle”, “Guam”, etc.

• Statistical units
• “Tract”, “Block Group”, “MSA”

• At block level, short form data only
• Why??

US->Region->Division

State->County->County subdivision

Place->Census tract->Block group->Block

• “Federal Information Processing Standard”
• Example: 060710036021003

MSAs

But in New England = NECTA

TAZs

Voting districts

Tribal lands

[PUMA – public use microdata areas]

These are the „spatial data‟ for the census

Roads, census administrative units, streets addresses, points of interest, other administrative units, hydrography, physical features.

Organized as line, landmark, polygon features

Key feature: TOPOLOGY!

Must not overlap

Must be covered by (class of)

Must cover each other

Can be used to describe ALL relationships between census geographies!

Features are defined as 0-cell (point), 1-cell (line), 2-cell (polygon)

• General case
• •From-Node
• •To-Node
• •Left-Polygon
• •Right-Polygon

->

Specific TIGER terms

• •From Address Right
• •To Address Right
• •From Address Left
• •To Address Right

• •Line
• •Landmark
• •Polygon
• •But: these are not „features‟ like we talk about them in a GIS sense! Loosely, feature is something that has real world identity here.

A summary file is a collection of data from the Census

SF1 = all short form data

SF2 = all short form data, by race

SF3 = all long form data

SF4 = all long form data, by race

• PUMS = raw data, long form
• Requires special permission to access

Each attribute has a numerical code (i.e. “H903633”), not a logical name.

Pxxxxxx is always population

Hxxxxxx is always housing

Pxxxxx1 is almost always total pop

Hxxxxx1 is almost always total number of households

Census metadata tells you what the codes stand for!

Short form only - no long form

• American Community Survey (ACS) to collect info formerly on long form
• Continuously, rather than ever 10 yrs

2 new questions to try to get at short-term household members (i.e. „anyone who sometimes lives somewhere else?‟

This will have implications for how Summary Files are organized/released!

• 10-year gap between censuses
• American Community Survey
• Long form, annually, to sample of HHs
• More follow-up with respondents

• Undercounts
• Demographic data (births, deaths, estimates of undoc‟d immigrants, etc.)
• Post census surveys (Post-Enumeration Survey)

• Integrate other administrative data
• IRS, County Registrar, Postal Service, etc.

Harvest & assemble online personal data

• Barriers / Challenges
• Legal / societal
• Technical

Response rate typically about 66%, based on master address file (MAF)

In-person enumeration brings this to 97%

So how do we come up with the other 3%?

Were all items completed by the other 97%? Probably not…

Use of statistical methods to adjust for missing and inconsistent responses

• “Hot deck imputation” - sampling a given set of responses from a particular geographic area over and over and using those responses to do several things:
• Correct for non-response
• Perform edits
• Ensure confidentiality

Depends on assumptions about homogeneity (near things are more similar than far…)

• Two major types:
• Allocation (some missing values entered based on other reported information for the person or household, or similar others)
• Substitution (all information for a person or household is created from others with similar characteristics

Less likely to respond to Census: highly transitory HHs, non-English speakers, undocumented individuals.

Not all parts of US have similar rates of imputation (pop and race more likely to have been allocated in SW)

What does this mean for you as a user of US Census data?

• Shortest path
• Shortest/fastest way to get home from school?

• Closest facility
• Which post office is closest to my house?

• Service area
• If people who can get to our store within 30 minutes will shop here, what is our service area?

• Analyze transit "cost"
• How long does it take to navigate through this network?

• Select optimal routes
• What is the least cost path through this network (time, distance, traffic…).

• Solve resource allocation problems
• What is the portion of the network that should be considered the „territory‟ from a particular starting point (e.g. each fire station…)

Context: Gov‟t sets benchmark for green space per capita, urban and regional planners must meet this standard

But this may not reflect ability of population to travel to available sites

Buffering the sites or calculating straight-line distance doesn‟t represent how people actually travel to the sites

A network-based solution is required

Define “access points” for the green spaces

Establish centroids for the census polygons (or whatever areas you are using as the „source areas‟ that people are coming from)

For all points in demographics layer, calculate distance to nearest point in parks layer, following the network.

Assess how the "nearest park" distances may differ for different population groups

Network: a system of linear features connected at intersections and interchanges

A network is composed of a set of nodes and the links that connect them

Networks might be used to represent: roads, streams, airline flight paths, railroads and so on.

Not all sets of lines in a GIS constitute a network

• Defining a network involves adding additional information that tells us:
• "Cost" to travel each link
• Connectivity between links
• Direction that may be traveled on each
• Limits on transfer from one link to another

• If it is a road network you need systems for:
• Representing overpass/underpass
• Accounting for time taken in a turn
• All possible turns at an intersection
• One-way and other street direction limitations

• Overpass & underpass
• a) Can simply cross the arcs with no node (easy)
• b) Can insert two nodes with elevation values to show which is the overpass and which is the underpass (harder)

• Link impedance – "cost" (time) of traversing a "link" (a segment separated by 2 nodes)
• To a certain degree, length of link is used
• May depend on speed limits, traffic conditions, etc

Turn impedance – „cost‟ of transitioning from one arc to another in the network (a turn). Requires a „turn table‟, because different possible turns may have different impedence (right vs. unprotected left, for instance).

• Modeling events that happen along the network
• Clusters of traffic accidents, flight delays

• Multiple modes of travel on the network
• Bike to the train station, train to work

• Attributes of the network
• Risk of a toxic spill, traffic volumes

• Distance and speed are not the only factors influencing route selection
• Road, amenities, terrain/view, weather, elevation, etc.

Conventional impedance models limited for transportation planning

• But, additional attributes can be handled as impedance factors, combined
• Sadeghi-Niaraki et al. 2011.

A data model built on lines of a network

Uses relational database to store network geometry (intersections, streets) and info about traversing the network (turn tables, link impedance tables, etc)

You can add other attribute data to your model (pavement conditions - dry, wet, icy; risk of an accident; frequency of monsoon flooding, etc.)

Modeling multi-mode travel

Dispatching „toxic trucks‟ along less risky routes

Evacuation planning

•  One kind of transportation
•  Single trip only
•  Different start/stop points
•  Many travel behaviors not covered bythis kind of model…

Multiple copies of networks, one for each mode of travel (driving, pub transit, walking etc.)

The networks intersect @ nodes where you can switch from one mode to another (transition links)

locations on network have attributes based on mode of travel and type of trip.

Size of network model is untenable

Simplify with user input selecting parts of the network?

• Place-based differences in actual travel options and patterns
• Areas w/o public transit

How do we model risks along a road network so we can route vehicles on the least risky path?

Involves multi-criteria modeling

• Assessing „risk‟ is tricky, esp in GIS
• How much is too much? Costs?
• Does risk depend on toxicity, likelihood of event, # of people effected, something else?

• Created measures for these variables
• Population density, traffic flow, traffic speed, emergency response time, sparse population…

Each variable given an ordinal rank

Scores are weighted to calculate composite risk; risk score assigned to each segment

Routing algorithm uses criteria scores to determine optimal route

Buffer the „at risk‟ road segments

Identify vulnerable facilities w/in the buffers (kids, elderly, high pop density facilities)

Clip / interpolate socio-economic data using the buffers

Networks, as part of mathematical and spatial models.

• Challenges:
• Real-time data? [sensors]
• Uneven info access / unpredictable behavior
• Damage to network
• Multi-institutional data collection and sharing

Location-based services

GPS-enabled devices (mostly cell phones, also cars)

• Where is the device in the network, where is it relative to other locations in the network?
• Location-based services

• Most use street networks as a basis:
• Where is person or object X in relation to street network? How near or far?
• What is the shorter route between two places on this network? Fastest?
• How many people/objects can move along this network under certain conditions?