gifpro: Commercial area prognosis
In REAT: Regional Economic Analysis Toolbox

Description Usage Arguments Details Value Author(s) References See Also Examples

This function contains the basic GIFPRO model for commercial area prognosis (GIFPRO = Gewerbe- und Industrieflaechenprognose)

1 2	gifpro(e_ij, a_i, sq_ij, rq_ij, ru_ij = NULL, ai_ij, time.base, tinterval = 1, industry.names = NULL, output = "short")

`e_ij`	a numeric vector with i values containing the current employment in i industries in region j
`a_i`	a numeric vector with i values containing the share of employees in industry i which is located in commercial areas
`sq_ij`	a numeric vector with i values containing the annual quote of resettled employees (Neuansiedlungsquote in German) in industry i, in percent
`rq_ij`	a numeric vector with i values containing the annual quote of relocated employees (Verlagerungsquote in German) in industry i, in percent
`ru_ij`	a numeric vector with i values containing the annual quote of employees in industry i which is located in reused commercial area (Wiedernutzungsquote in German), in percent (default: `ru_ij = NULL`, which represents a quote of 0 percent, meaning that no commercial area can be reused)
`ai_ij`	a numeric vector with i values containing the areal index (Flaechenkennziffer in German), representing the area requirement (e.g. in sqm) per employee in industry i
`time.base`	a single value representing the start time of the prognose (typically current year + 1)
`tinterval`	a single value representing the forecast horizon (length of time into the future for which the commercial area prognosis is done), in time units (e.g. `tinterval = 10` = 10 years)
`industry.names`	a vector containing the industry names (e.g. from the relevant statistical classification of economic activities)
`output`	Type of output: `output = "short"` (default) shows the final number of relevant employment and commercial area requirement. If `output = "full"`, employment and commercial area are displayed for each time unit (year)

In municipal land use planning (mostly in Germany), the future need of local commercial area (which is a type of land use, defined in official land-use plans) is mostly forecasted by models founded on the GIFPRO model (Gewerbe- und Industrieflaechenbedarfsprognose, prognosis of future demand of commercial area). GIFPRO is a demand-side model, which means predicting the demand of commercial area based on a prognosis of future employment in different industries (Bonny/Kahnert 2005). The key parameters of the model are the (assumed) shares of employees located in commercial areas (a_i), the (assumed) quotas of resettlement (sq_{ij}), relocation (rq_{ij}) and (sometimes) reuse (ru_{ij}) as well as the (assumed) area requirement per employee (ai_{ij}). Outgoing from current employment in i industries in region j, e_{ij}, the future employment is predicted based on the quotas mentioned above and, finally, multiplied by the industry-specific (and maybe region-specific) areal index. The GIFPRO model has been modified and extended several times, especially with respect to industry- and region-specific employment growth, quotas and areal indices (Deutsches Institut fuer Urbanistik 2010, Vallee et al. 2012).

A list containing the following objects:

`components`	Matrices containing the single components (resettlement, relocation, reuse, relevant employment)
`results`	Matrices containing the final results per year and all over

Thomas Wieland

Bonny, H.-W./Kahnert, R. (2005): “Zur Ermittlung des Gewerbeflaechenbedarfs: Ein Vergleich zwischen einer Monitoring gestuetzten Prognose und einer analytischen Bestimmung”. In: Raumforschung und Raumordnung, 63, 3, p. 232-240.

Deutsches Institut fuer Urbanistik (ed.) (2010): “Stadtentwicklungskonzept Gewerbe fuer die Landeshauptstadt Potsdam”. Berlin. https://www.potsdam.de/sites/default/files/documents/STEK_Gewerbe_Langfassung_2010.pdf (accessed October 13, 2017).

Vallee, D./Witte, A./Brandt, T./Bischof, T. (2012): “Bedarfsberechnung fuer die Darstellung von Allgemeinen Siedlungsbereichen (ASB) und Gewerbe- und Industrieansiedlungsbereichen (GIB) in Regionalplaenen”. Im Auftrag der Staatskanzlei des Landes Nordrhein-Westfalen. Abschlussbericht Oktober 2012. Aachen.

gifpro.tbs, portfolio, shift, shiftd, shifti

# Data for the city Kempten (2012):
emp2012 <- c(7228, 12452, 11589)
sharesCA <- c(100, 40, 10)
rsquote <- c(0.3, 0.3, 0.3)
rlquote <- c(0.7, 0.7, 0.7)
arealindex <- c(148, 148, 148)
industries <- c("Manufacturing", "Wholesale and retail trade, Transportation 
and storage, Information and communication", "Other services")

gifpro (e_ij = emp2012, a_i = sharesCA,  sq_ij = rsquote,
rq_ij = rlquote, ai_ij = arealindex, time.base = 2012, 
tinterval = 13, industry.names = industries, output = "short")
# short output

gifpro (e_ij = emp2012, a_i = sharesCA,  sq_ij = rsquote,
rq_ij = rlquote, ai_ij = arealindex, time.base = 2012, 
tinterval = 13, industry.names = industries, output = "full")
# full output

gifpro_results <- gifpro (e_ij = emp2012, a_i = sharesCA,  sq_ij = rsquote,
rq_ij = rlquote, ai_ij = arealindex, time.base = 2012, 
tinterval = 13, industry.names = industries, output = "short")
# saving results as gifpro object

gifpro_results$components
# single components

gifpro_results$results
# results (as shown in full output)