gifpro.tbs {REAT}

Trend-based and location-specific commercial area prognosis

Description

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)

Usage

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")

Arguments

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)

Details

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).

Value

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

Author(s)

Thomas Wieland

References

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.

See Also

gifpro, portfolio, shift, shiftd, shifti

Examples

# 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)