Calculating Geographical Diffusion
Arguments
- df
data frame to read in. Data frame should include a variable that is a character list of each observation's neighbors.
- id
the grouping variable, usually states or counties
- neighbors
a variable that is a
characterlist of each observation's neighbors. The elements of the character list of neighbors should be separated by commas.- time
the time variable, at which observations are measured.
- status
binary, user-defined measure of the status of policy or event in a state in a given year.
0equates to policy has not yet occurred in the year, for the state,1equates to policy event has already been adopted in the year, for the state – a value of1should exist for a state in the year it was adopted and every year thereafter). The example below relies on ERA ratification data from Soule and King (2006) <doi:10.1086/499908>, merged with ideology data from Berry et al. (1998) <doi:10.2307/2991759>, but the user should include the measure of adoption of their choice.- end
logical (default set to
F). When set toend = T, will calculate the percent of neighbors that had adopted policy by year-end. Otherwise, will calculate based on number of neighbors that had adopted the policy at year-start.- keep
logical (default set to
F). When set toend = T, will include additional variables (number of neighbors and number of neighbors that had adopted the policy) in the updated data frame.
Value
This function updates the data frame with a new variable capturing the geographical diffusion score.
References
Berry, William D., Ringquist, Evan J., Fording, Richard C.,
and Hanson, Russell L.
(1998) 'Measuring Citizen and Government Ideology
in the American States, 1960-93.'
American Journal of Political Science 42:327-348.
doi:10.2307/2991759
.
Soule, Sarah A., and King, Brayden G.
(2006) 'The Stages of the Policy Process
and the Equal Rights Amendment, 1972-1982.'
American Journal of Sociology 111:1871-1909.
doi:10.1086/499908
.
This function calculates the percent (or proportion) of geographically contiguous neighbors that have engaged in some event (e.g. policy adoption) in a given year. This function can be applied to any unit of analysis and time level for any type of event.