gifpro.tbs: Trend-based and location-specific commercial area prognosis
In REAT: Regional Economic Analysis Toolbox

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

This function contains the TBS-GIFPRO model for commercial area prognosis (TBS-GIFPRO = Trendbasierte und standortspezifische Gewerbe- und Industrieflaechenprognose; trend-based and location-specific commercial area prognosis)

gifpro.tbs(e_ij, a_i, sq_ij, rq_ij, ru_ij = NULL, ai_ij, 
time.base, tinterval = 1, prog.func = rep("lin", nrow(e_ij)), 
prog.plot = TRUE, plot.single = FALSE,
multiplot.col = NULL, multiplot.row = NULL,
industry.names = NULL, emp.only = FALSE, 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)
`prog.func`	a vector containing the estimation function types for employment prognosis ("lin" for linear, "pow" for power, "exp" for exponential and "logi" for logistic function); must have the same length as `e_ij` and `industry.names`, respectively
`prog.plot`	Logical argument that indicates if the employment prognoses have to be plotted
`plot.single`	If `prog.plot = TRUE`: Logical argument that indicates if the plots are stored as single graphic devices or integrated in one plot
`multiplot.col`	No. of columns in plot
`multiplot.row`	No. of rows in plot
`industry.names`	a vector containing the industry names (e.g. from the relevant statistical classification of economic activities)
`emp.only`	Logical argument that indicates if the analysis only contains employment prognosis
`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`	List with matrices containing the single components (resettlement, relocation, reuse, relevant employment)
`results`	List with matrices containing the final results per year and all over as well as the industry-specific forecast data

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.

gifpro, portfolio, shift, shiftd, shifti

# Data for Goettingen:
data(Goettingen)

anteileGOE <- rep(100,15)
nvquote <- rep (0.3, 15)
vlquote <- rep (0.7, 15)

gifpro.tbs (e_ij = Goettingen[2:16,3:12], 
a_i = anteileGOE, sq_ij = nvquote,
rq_ij = vlquote, tinterval = 12, prog.func = 
rep("lin", nrow(Goettingen[2:16,3:12])),
ai_ij = 150, time.base = 2008, output = "full",
industry.names = Goettingen$WZ2008_Code[2:16],
prog.plot = TRUE, plot.single = FALSE)