Feasibility Study, New Shopping Center in Madrid. Practical Case. Big Data. This study is part of an IOT project by ("Wi-Sen.com.es") Second Part

1. Por: Oscar Cuenca Roca
Tecnología WI-SEN
P r a c t i c a l c a s e
RETAILFEASIBILITY AND CONVENIENCE STUDY
Opening Shopping Center
Madrid-España
linkedin.com/in/cuencaoscar
S E C O N D D E L I V E R Y
2

2. Determination of variables.
Classification and calculation of data.
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CUENCA
Óscar
WI-SEN TECHNOLOGY
Now that in our first module we have already defined the main objective of the study
and the method we are going to use, we proceed to study the variables that will help to find
the "K" load and other other keys to better understand the viability of a center commercial.
Registration: First module:
https://www.slideshare.net/OscarCuenca1/estudio-viabilidad-big-data-y-centros-comerciales-1
This module aims to select the variables that are related to shopping centers.
In order to be able to use these variables, we must have all the data and those that we do not
have to find by approximation or by prediction.
At first these are the data we have of the three possible shopping centers.

3. Note 1. Type of shopping center has been normalized to its size
not taking into account another factor in order to be able to
process data in a homogeneous way
DATA PROCESSING
WI-SEN
TECHNOLOGY
Por: Oscar Cuenca Roca
Note 2. The rental price is based on an approximate price per m2
for an area of 40 m2. This price changes a lot depending on the
square meters to rent, for that reason, to standardize again the
data we have selected the rental price according to an estimate
of 40 m2 to rent
Note 3. Both in stores and in restaurant we have selected the
three most well-known shops in general terms and the three
most popular restaurants
These are the shopping centers that we will work as shopping centers in the area of Madrid
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4. WI-SEN
TECNOLOGÍA
Por: Óscar Cuenca Roca
91already existing
SHOPPING CENTERS
and we will make the projection of the
3 Commercial Centers
that our client proposes To determine which is the most viable,
convenient and profitable option.
in total we will analyze
What data we are going
to handle
Let's split the data into two
large tables. A table that
will be a photograph to 2016
and another that will be a
history

5. analysis
WI-SEN
TECHNOLOGY
Por: Óscar Cuenca Roca
BIG
DATA
Variables of the first table. 2016 per shopping center

6. Data to Analyze:
CUENCA
Óscar
TECHNOLOGY WI-SEN

7. CUENCA
Óscar
TECHNOLOGY WI-SEN
Once we have already determined the variables. We will try to calculate data that we could
not find and that we will need in our study.
In our first table we have the "inflow people" as the first variable. In this column we need
data more data since it is not complete.
Variables second table Historical by area of influence of each shopping center::

8. METODOLOGY
In this case there are no 2
numerical axes directly related
as could be the case of rental
price and common expenses.
This forces us to use other
techniques. In our case:
We will split the centers in
• Urban
• Semi Urban
• Extra Radio
We have approximately 50% complete data on the
annual inflow people to shopping centers. In order
to use TableCurve 2D and generate an equation that
gives us a formula capable of delivering
"inflow people" approximations, we would
need numerical data on the two axes, X and Y.
TECHNOLOGY
Oscar CuencaTECHNOLOGY WI-SEN
To find the missing Data of public influx
We have observed that there is continuity in the volume of influx depending on whether it is
urban or other one. This relationship is clearer than if one tries to see some relation with the
affluence and the size of the center. Also the number of parking spaces is not determinant to
predict the influx although it would seem that it should be directly proportional.
The explanation is that in urban and semi-urban centers the parking spaces are limited due
to the lack of available space, the lack of this type of service in the province of Madrid and
especially in the city of Madrid is a fact.
Once we obtain the average by type of center of affluence (only the data available as is
obvious), we then order them by their size, and here we see in what proportion it varies
according to its size with respect to the mean and we apply its correlation

9. Oscar CuencaTECNOLOGY WI-SEN
Table before ordering:
Table Resolved:

10. Oscar CuencaTECNOLOGY WI-SEN
The next column where we do not have data is that of common expenses
Methodology To find Missing Data in our Common Expenses column..
The data we had were the
rental prices per m2 for a
40m2 premises in all the
shopping centers that are
part of the province of
Madrid.
The data that we had of
common expenses were
incomplete so we wanted
to find this data in a
predictive way from the
representation of a curve
that reflects the data we
had and provide us with a
function capable of
shedding that missing data
since we did not have
sufficient information to
determine such costs from
a qualitative point of view
we had to deal with this
problem from a
quantitative point of view
We have used the Kinetic
equation and the TableCurve
2D software to find this
missing data. The missing
data are divided into 3 groups
to do the calculations
separately as the nature of
these groups varies s
ubstantially from one to
another. This division is:
The result was as follows.
• Urban centers
• Semi Urban Centers
• Off-radio centers
Original table, column G corresponds to common expenses and F to
rental prices per m2 for 40 m2:

11. Oscar CuencaTECNOLOGY WI-SE
Final Table:
We use the TableCurve 2D software..

12. Oscar CuencaTECNOLOGY WI-SEN
Using the Kinetic Equationso
The kinetic exchange models are dynamic multi-agent
models inspired by the statistical physics of energy
distribution, which try to explain the robust and
universal characteristics of income / wealth distributions
that can well be extrapolated from a rental price /
common expenses in our case private variables
(income / wealth) and (rent price / common expenses)
a real relationship between both.
Given that income / wealth distributions and in the
case that we treat rental prices / common expenses
are the results of the interaction between many
heterogeneous agents, there is an analogy with
mechanical statistics, in which many particles interact.
This similarity was observed by Meghnad Saha and B. N. Srivastava in 1931 [1]
and thirty years later by Benoit Mandelbrot. In 1986, an elementary version of the exchange
model was first proposed by J. Angulo [3] Although this theory has originally been derived
from the principle of maximizing the entropy of mechanical statistics, it has recently been
shown [4] that it could also derive from the principle of utility maximization, following a
standard exchange model with the function of Cobb-Douglas. The exact distributions
produced by this class of kinetic models are only known at certain limits and extensive
research has been done on the mathematical structures of this class of models [5] [6].
The general forms have not been derived so far

13. Oscar CuencaTECNOLOGY WI-SEN
Study of Common Expenses for Urban Centers

14. Oscar CuencaTECNOLOGY WI-SEN
Study of common expenses for outside city centers

15. Oscar CuencaTECNOLOGY WI-SEN

16. Oscar CuencaTECNOLOGY WI-SEN
Study of common expenses for semi-urban centers

17. Oscar CuencaTECNOLOGY WI-SEN
[[1] Saha, M.; Srivastava, B.N. (1931). A Treatise on Heat. Indian Press (Allahabad). p. 105.
(the page is reproduced in Fig. 6 in Sitabhra Sinha, Bikas K Chakrabarti, Towards a physics of
economics, Physics News 39(2) 33-46, April 2009)
[2] Mandelbrot, B.B. (1960). "The Pareto-Levy law and the distribution of income". International
Economic Review. 1: 69. doi:10.2307/2525289.
[3] Angle, J. (1986). "The surplus theory of social stratification and the size distribution of
personal wealth". Social Forces. 65 (2): 293–326. JSTOR 2578675. doi:10.2307/2578675
[4] A. S. Chakrabarti; B. K. Chakrabarti (2009). "Microeconomics of the ideal gas like market
models". Physica A. 388: 4151–4158. doi:10.1016/j.physa.2009.06.038.
[5] Jump up ^ During, B.; Matthes, D.; Toscani, G. (2008). "Kinetic equations modelling wealth
distributions: a comparison of approaches". Physical Review E. 78: 056103. doi:10.1103/
physreve.78.056103.
[6] Jump up ^ Cordier, S.; Pareschi, L.; Toscani, G. (2005). "On a kinetic model for a simple
market economy". Journal of Statistical Physics. 120: 253–277. doi:10.1007/s10955-005-5456-0.

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Ó S C A R C U E N C A R O C A
C h i e f N e w B u s i n e s s a n d I n n o v a t i o n
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OCTOBER 13 2017
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