adv gis quiz 2 Flashcards
accessibility
relative ease by which the locations of activities such as work, school, shopping, recreation, and health care can be reached from a given location
why measuring accessibility is important
- resources are scarce
- not uniformly distributed
- disadvantaged groups might suffer more from poor access
how accessibility is determined
distributions of supply and demand and how they are connected in space
four categories access can be classified into
potential vs revealed access
spatial vs aspatial access
revealed accessibility
actual use of a service
potential accessibility
probable utilization of a service
spatial access
emphasizes importance of a spatial separation between supply and demand as a barrier or facilitator
aspatial access
stresses nongeographic barriers or facilitators
supply-demand ratio method
used before gis, computes ratio of supply vs demand to measure accessibility
floating catchment area (FCA) method
catchment area floats from one residential location to another across the study area, and defines the accessibility for all locations
problems with FCA
- assumes services within a catchment area are fully available to residents within that catchment area and residents use only those services
- doesn’t account for competition, which can decrease demand
2SFCA - two step floating catchment area
repeats the process of floating catchment twice (for each supply location, search demand locations and for each demand location, search supply locations)
considers interactions between demands and suppliers
how to measure catchment area for 2SFCA
use travel time NOT euclidean distance
General Transit Feed Specification (GTFS)
incorporates data describing transit trips including route info, schedules, patterns, stops, services, trip paths, fares, and agency contact info
what is trade area analysis necessary for
site selection of a retail store
trade area
geographic area from which the store draws most of its customers and within which market penetration is highest
spatial interaction
realized flow of passengers or freight between an origin and a destination
transport demand/supply relationship expressed over a geographical space
complementarity (conditions for spatial flows)
there must be a supply and demand between the interacting locations
intervening opportunity (conditions for spatial flows)
a location may offer a better alternative as a point of origin or as a point of destination
transferability (conditions for spatial flows)
mobility must be supported by transport infrastructure, implying that the origin and the destination must be linked
origin/destination matrices
require directional flow information between a series of locations
gravity model
most common formulation of spatial interaction method
attraction between two objects is proportional to their mass and inversely proportional to their respective distance
ex. NYC and London have their own pops and they are ___ miles away, so multiply their pops and divide by miles to determine interaction strength
analog method and regression models
uses existing store or several stores as analogs to forecast sales in a proposed similar facility
Y = b0 + b1x1 + b2x2 + ….
Y = store profits/sales
x = explanatory variables
b = regression coefficients to be estimated
proximal area method
assumes consumers choose the nearest store among similar outlets
proximal area method - consumer based
begins with consumer location and searches for nearest store
consumers that share the same nearest store constitute the proximal area for that storeq
proximal area method - stores based
constructs thiessen polygons from the store locations, and the polygon around each store defines the proximal area for that store
layer of thiessen polygons may then be overlaid with that of consumers to identify demographic structures within each proximal area
reilley’s law of retail gravitation
consider both distance (or time) from and attractions (better prices or goods, etc) of stores
reilly’s law of retail gravitation equation
S1/d1x^2 = S2/d2x^2
factors that influence trade areas
population of community
proximity of other competing business districts
mix of businesses in your community
attractions
traffic patterns
convenience trade areas
based on the purchase of products and services needed on a regular basis
destination trade area
based on the purchase of “major” products and services (like ikea)
focus group use to define trade area
have participants shade on the map where they think the convenience and destination trade areas lie and use consensus
simple rings - Defining Trade Areas Using GIS
choosing a certain distance radius
data driven rings - Defining Trade Areas Using GIS
based on business district values such as volume of sales, store size, or number of stores rather than distance
drive time polygons - Defining Trade Areas Using GIS
how long it takes to drive somewhere (ex downtown is a much smaller polygon than suburbs since you go much less distance over time in the downtown)
thiessen polygons - Defining Trade Areas Using GIS
use thiessen polygons
gravity modeling
probability of customer choosing a certain place relative to the other available places
customer street address or zip code - Defining Trade Areas Using GIS
using street address or zip code to see what customers are coming from what areas
regression analysis function
used to specify and test a functional relationship between variables
why use regression?
explore correlations
predict unknown values
understand key factors
regression equation
mathematical formula applied to the explanatory variables to best predict the dependent variable you are trying to model
y = b0 + b1x1 + b2x2 + ….
y=dependent variable
x=independent variable
b=regression coefficient
regression coefficients
computed by the regression tool. they are values, one for each explanatory variable, that represent the strength and the type of relationship the explanatory variable has to the dependent variable
p-value
small = small probabilities and suggest that the coefficient is actually important to your model
r squared
quantify model performance (closer to 1 the better the line fits the data)
residuals
difference between an observed outcome and its predicted value using the regression equation is the residual
ordinary least squares (OLS)
most common regression technique
regression line is chosen because it is the line that comes closes to most of the points
six statistical checks
- are the explanatory variables helping your model? (check asterisk)
- are the relationships expected? (check sign)
- are any of the explanatory variables redundant? (check VIF)
- is the model biased? (p-value for the Jarque-Bera test)
- are all key explanatory variables included in the model? (check residuals)
- how well is the model explaining the dependent variable? (r squared and AIC)
Global Moran’s I statistic null hypothesis
attribute being analyzed is randomly distributed among the features in the study area
geographically weighted regression (GWR)
provides a local model of the variable or process you are trying to understand/predict by fitting a regression equation to every feature in the dataset
how GWR works
constructs separate equations by incorporating the dependent and explanatory variables of the features falling within the neighborhood of each target feature
the shape and extent of each neighborhood analyzed is based on the neighborhood type and neighborhood selection method parameters
number of neighbors
neighborhood size is a function of a specified number of neighbors, which allows neighborhoods to be smaller where features are dense and larger where features are sparse
distance band
neighborhood size remains constant for each feature in the study area, resulting in more features per neighborhood where features are dense and fewer per neighborhood where they are sparse
local weighting scheme
weights are determined using a kernel, which is a distance decay function that determines how quickly weights decrease as distances increase
two kernel options in local weighting scheme parameter
gaussian weight
bi-square weight
