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")a numeric vector with \(i\) values containing the current employment in \(i\) industries in region \(j\)
a numeric vector with \(i\) values containing the share of employees in industry \(i\) which is located in commercial areas
a numeric vector with \(i\) values containing the annual quote of resettled employees (Neuansiedlungsquote in German) in industry \(i\), in percent
a numeric vector with \(i\) values containing the annual quote of relocated employees (Verlagerungsquote in German) in industry \(i\), in percent
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)
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 \(i\)
a single value representing the start time of the prognose (typically current year + 1)
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)
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
Logical argument that indicates if the employment prognoses have to be plotted
If prog.plot = TRUE: Logical argument that indicates if the plots are stored as single graphic devices or integrated in one plot
No. of columns in plot
No. of rows in plot
a vector containing the industry names (e.g. from the relevant statistical classification of economic activities)
Logical argument that indicates if the analysis only contains employment prognosis
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)
A list containing the following objects:
List with matrices containing the single components (resettlement, relocation, reuse, relevant employment)
List with matrices containing the final results per year and all over as well as the industry-specific forecast data
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).
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) (Deutsches Institut fuer Urbanistik 2010).
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.
# NOT RUN {
# 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)
# }
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