The Multicriteria decision analysis is a tool to support decision-making in the identification of areas with the utmost beekeeping potential.
This paper design a GIS multicriteria approach to assess the beekeeping potential. The development of a conceptual model structure
requires the participation of stakeholders and experts in that process. The spatial Multicriteria Decision Analysis (MCDA) allowed defining
the potential beekeeping map. The resulting maps can be used by the beekeepers associations to easily select the more suitable areas for the
apiaries location or relocation and avoid prohibited areas by legal requirements

Anjos, O.

Fernandez, P.

Roque, N.

Fernandez, Paulo

Roque, Natália

Repositório do Instituto Politécnico de Castelo Branco

1  Introduction The beekeeping plays an important role within the rural areas because the hive products offer an additional income to the populations [1]. The beekeeping planning is greatly affected by spatial factors; therefore Geographical Information System (GIS) are a powerful tool to overlap and relate a variety of spatial data levels on a map, and consequently a very useful tool for beekeeping activity planning. The GIS have been applied in a sparse beekeeping planning research studies [1, 2, 3]. The implementation of spatial analysis models based on GIS [3] resulted in a set of variables important to the beekeeping planning.  Multicriteria decision analysis (MCDA) can be defined as a group of methods and procedures for evaluating decision alternatives based on multiple conflicting criteria which aids in the selection of the best alternative. The effort to integrate MCDA into GIS has been contributory for developing the paradigm of spatial decision support, in which geographic information technology is made available directly to decision-makers for policy or scenario development [4]. The main purpose of this paper is to propose a methodology to assess the beekeeper potentiality using GIS and multicriteria analysis.  2 Data and Methods This work defines a methodology to assess the potential beekeeping activity in the Montesinho Natural Park (Figure 1) a region in the northwest of Portugal, managed by a beekeeper association. The area has 75,000 hectares with an enormous diversity of vegetation. The altitudes range from 1486 meters in the Montesinho Mountain and 438 meters in the Mente river (Douro basin).  The spatial multicriteria model is supported by the following spatial data: land use, Digital Terrain Model (DTM), river network, water body, road network; urban areas, sources of electromagnetic radiation, climatic variables, and apiary location. Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal) Paulo Fernandez  Instituto Politécnico de Castelo Branco - Escola Superior Agrária. Quinta da Senhora de Mércules, Apartado 119, 6001-909 Castelo Branco, Portugal.  ICAAM - Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora. Évora, Portugal  palex@ipcb.pt Natália Roque  Instituto Politécnico de Castelo Branco - Escola Superior Agrária. Quinta da Senhora de Mércules, Apartado 119, 6001-909 Castelo Branco, Portugal.  nroque@ipcb.pt Ofélia Anjos  Instituto Politécnico de Castelo Branco - Escola Superior Agrária. Quinta da Senhora de Mércules, Apartado 119, 6001-909 Castelo Branco, Portugal.  Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal  ofelia@ipcb.pt Abstract The Multicriteria decision analysis is a tool to support decision-making in the identification of areas with the utmost beekeeping potential. This paper design a GIS multicriteria approach to assess the beekeeping potential. The development of a conceptual model structure requires the participation of stakeholders and experts in that process. The spatial Multicriteria Decision Analysis (MCDA) allowed defining the potential beekeeping map. The resulting maps can be used by the beekeepers associations to easily select the more suitable areas for the apiaries location or relocation and avoid prohibited areas by legal requirements.  Keywords: Beekeeping, GIS, Montesinho Natural Park, Multicriteria decision analysis. Figure 1: Location of study area.  AGILE 2016 – Helsinki, June 14-17, 2016  The land use data (COS2007) was provided by the Direção-Geral do Território (DGT) and is a vector map with highly thematic detail. The DTM was produced by spatial interpolation of the altimetry data. The map of the radiation received by the terrain was based on the DTM. The river network, road network and urban areas were extracted from the topographic map and aerial images, and were subject to proximity analysis to obtain the map of distance for these features. The electromagnetic radiation (communication towers and electric power transmission network) was provided by the national authorities. The temperature and precipitation maps were extracted from [5].  The survey of the geographic location of the apiaries (Figure 2) was accomplished by a Global Positioning System (GPS), Trimble Juno 5 Series. The field work also allowed collecting information about the existing flora nearby the apiaries.   The GIS-MCDA is a process that transforms and combines spatial data and value judgments into a final decision [4] allowing for a better understanding of their multi-components. The criterion is one of a number of measures against which options are assessed and compared in the multicriteria analysis [6]. The structure of the proposed model which allowed the identification of criteria that reflect the beekeeping potential was classified as factors or constraints (Figure 3). A factor is a measure that enhances or detracts from the suitability of a specific alternative for the activity under analysis. In this case, the factors are: land use, solar radiation, distance to river network and water body, distance to sources of electromagnetic radiation, temperature, precipitation and flora in the apiaries vicinity. A constraint serves to affect or restrict the alternatives under consideration; it is an element or feature that represents limitations or restrictions and whether an area is considered unsuitable. This study uses legal constraints to affect the beekeeping potential (Figure 4). The methodological process has the following steps: (1) hierarchical structure of the beekeeping potential model; (2) standardisation of the criteria; (3) criteria weighting; (4) decision rules; and (5) mapping of beekeeping potential. The criteria were measured on different scales, so factor values must be standardised before combination. The standardisation, typically, uses the minimum and maximum value as scaling points. This process uses a range of fuzzy set membership functions to convert the criteria values into another comparable scale, expressing a fuzzy standardised scale of values. The purpose of assigning weights to the criteria is to express the degree of importance of each factor in relation to others in the evaluation process, and it is a challenge step in the decision-making process. The relative weight of the vulnerability criterion is estimated by the Analytic Hierarchy Process (AHP) a method originally developed by Saaty [7]. The AHP compares criteria pair wise and then computes overall relative weights based on aggregate calculations of all pair wise ratios [8].  The criteria weighting process was established by the judgments, according the following assumptions: (1) the Figure 2: Location of the apiaries. Figure 4: Legal constraints to beekeeping.   Figure 3: The factors and constraints to potential beekeeping assessment.  AGILE 2016 – Helsinki, June 14-17, 2016  differentiation of the criteria relative importance considered by a panel of experts, and (2) the empirical knowledge of bee masters. In this case, Weighted Linear Combination (WLC) was used to combine the criteria and produce the potential beekeeping map.  3 Results and discussion In the surrounding areas of urban areas and roads, apiaries can’t be installed due to legal framework. The constraints map obtained taking into account legal requirements is shown in Figure 4.  After the criteria aggregation was possible the creation of a beekeeping potential map (Figure 5) with a continuous scale range classified into five equal interval classes.  These results provide a framework, which may help policy makers, planners, beekeepers association, and bee masters to improve the beekeeping activity. In the study area 60% of the apiaries are located in high potential areas and the remaining apiaries should be relocated to improve the beekeeping activity.  Acknowledgments This work is funded by project “ApiSIG - Modelação espacial do potencial de exploração apícola utilizando sistemas de informação geográfica, do Programa Apícola Nacional”.  References [1] R. C .Estoque and Y. Murayama. Suitability analysis for beekeeping sites in La union, Philippines, using GIS &MCE techniques. Research Journal of Applied Sciences, 5, 242 – 253. 2010 [2] S. C. Camargo, R.C. Garcia, F. A. Vasconcelos, E. S. Pires, B. G. Hartleben, A. M., Moraes, F J. Oliveira, G. J. Luan, E.S. Mittanck, J. R. Gremaschi, D. J. Pereira. Implementation of a geographic information system (GIS) for the planning of beekeeping in the west region of Paraná. Anais da Academia Brasileira de Ciências, 86(2), 955-971. 2014 [3] O. Anjos, G. Silva, N. Roque, P. Fernandez. GIS based analysis to support the beekeeping planning: In: Karačonji, I.B., Lušić, D. (Eds.), International Symposium on Bee Products 3rd edition – Annual meeting of the International Honey Commission (IHC), Opatija, Croatia. 2014 [4] J. Malczewski. GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20, 703-726. 2006. [5] T. Monteiro-Henriques. Landscape and phytosociology of the Paiva River’s hydrographical basin. Ph.D. thesis. Instituto Superior de Agronomia , TULisbon. Lisboa. 2010 [6] J. Malczewski. GIS and Multicriteria Decision Analysis. John Wiley & Sons Inc, New York. 1999. [7] T. Saaty. A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281. 1977 [8] D. Schmoldt, J. Kangas, G.A. Mendoza, M. Pesonen. The Analytic Hierarchy Process in natural resource and environmental decision making. Kluwer Academic Publishers, Dordrecht, Netherlands. 2001   Figure 5: Map of beekeeping potential.  

Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal)

Repositório Científico do Instituto Politécnico de Castelo Branco

1  Introduction 
The beekeeping plays an important role within the rural 
areas because the hive products offer an additional income to 
the populations [1]. 
The beekeeping planning is greatly affected by spatial 
factors; therefore Geographical Information System (GIS) are 
a powerful tool to overlap and relate a variety of spatial data 
levels on a map, and consequently a very useful tool for 
beekeeping activity planning. 
The GIS have been applied in a sparse beekeeping planning 
research studies [1, 2, 3]. The implementation of spatial 
analysis models based on GIS [3] resulted in a set of variables 
important to the beekeeping planning.  
Multicriteria decision analysis (MCDA) can be defined as a 
group of methods and procedures for evaluating decision 
alternatives based on multiple conflicting criteria which aids 
in the selection of the best alternative. The effort to integrate 
MCDA into GIS has been contributory for developing the 
paradigm of spatial decision support, in which geographic 
information technology is made available directly to decision-
makers for policy or scenario development [4]. 
The main purpose of this paper is to propose a methodology 
to assess the beekeeper potentiality using GIS and 
multicriteria analysis. 
 
2 Data and Methods 
This work defines a methodology to assess the potential 
beekeeping activity in the Montesinho Natural Park (Figure 1) 
a region in the northwest of Portugal, managed by a beekeeper 
association. The area has 75,000 hectares with an enormous 
diversity of vegetation. The altitudes range from 1486 meters 
in the Montesinho Mountain and 438 meters in the Mente 
river (Douro basin). 
 
The spatial multicriteria model is supported by the 
following spatial data: land use, Digital Terrain Model 
(DTM), river network, water body, road network; urban areas, 
sources of electromagnetic radiation, climatic variables, and 
apiary location. 
Spatial multicriteria decision analysis to potential beekeeping 
assessment. Case study: Montesinho Natural Park (Portugal) 
Paulo Fernandez 
 
Instituto Politécnico de 
Castelo Branco - Escola 
Superior Agrária. Quinta da 
Senhora de Mércules, 
Apartado 119, 6001-909 
Castelo Branco, Portugal. 
 
ICAAM - Instituto de 
Ciências Agrárias e 
Ambientais Mediterrânicas, 
Universidade de Évora. 
Évora, Portugal 
 
palex@ipcb.pt 
Natália Roque 
 
Instituto Politécnico de 
Castelo Branco - Escola 
Superior Agrária. Quinta da 
Senhora de Mércules, 
Apartado 119, 6001-909 
Castelo Branco, Portugal. 
 
nroque@ipcb.pt 
Ofélia Anjos 
 
Instituto Politécnico de 
Castelo Branco - Escola 
Superior Agrária. Quinta da 
Senhora de Mércules, 
Apartado 119, 6001-909 
Castelo Branco, Portugal. 
 
Centro de Estudos 
Florestais, Instituto Superior 
de Agronomia, Universidade 
de Lisboa, Tapada da Ajuda, 
1349-017 Lisboa, Portugal 
 
ofelia@ipcb.pt 
Abstract 
The Multicriteria decision analysis is a tool to support decision-making in the identification of areas with the utmost beekeeping potential. 
This paper design a GIS multicriteria approach to assess the beekeeping potential. The development of a conceptual model structure 
requires the participation of stakeholders and experts in that process. The spatial Multicriteria Decision Analysis (MCDA) allowed defining 
the potential beekeeping map. The resulting maps can be used by the beekeepers associations to easily select the more suitable areas for the 
apiaries location or relocation and avoid prohibited areas by legal requirements. 
 
Keywords: Beekeeping, GIS, Montesinho Natural Park, Multicriteria decision analysis. 
Figure 1: Location of study area. 
 
AGILE 2016 – Helsinki, June 14-17, 2016 
 
The land use data (COS2007) was provided by the Direção-
Geral do Território (DGT) and is a vector map with highly 
thematic detail. The DTM was produced by spatial 
interpolation of the altimetry data. The map of the radiation 
received by the terrain was based on the DTM. The river 
network, road network and urban areas were extracted from 
the topographic map and aerial images, and were subject to 
proximity analysis to obtain the map of distance for these 
features. The electromagnetic radiation (communication 
towers and electric power transmission network) was provided 
by the national authorities. The temperature and precipitation 
maps were extracted from [5].  
The survey of the geographic location of the apiaries 
(Figure 2) was accomplished by a Global Positioning System 
(GPS), Trimble Juno 5 Series. The field work also allowed 
collecting information about the existing flora nearby the 
apiaries. 
 
 
The GIS-MCDA is a process that transforms and combines 
spatial data and value judgments into a final decision [4] 
allowing for a better understanding of their multi-components. 
The criterion is one of a number of measures against which 
options are assessed and compared in the multicriteria 
analysis [6]. The structure of the proposed model which 
allowed the identification of criteria that reflect the 
beekeeping potential was classified as factors or constraints 
(Figure 3). A factor is a measure that enhances or detracts 
from the suitability of a specific alternative for the activity 
under analysis. In this case, the factors are: land use, solar 
radiation, distance to river network and water body, distance 
to sources of electromagnetic radiation, temperature, 
precipitation and flora in the apiaries vicinity. A constraint 
serves to affect or restrict the alternatives under consideration; 
it is an element or feature that represents limitations or 
restrictions and whether an area is considered unsuitable. This 
study uses legal constraints to affect the beekeeping potential 
(Figure 4). 
The methodological process has the following steps: (1) 
hierarchical structure of the beekeeping potential model; (2) 
standardisation of the criteria; (3) criteria weighting; (4) 
decision rules; and (5) mapping of beekeeping potential. 
The criteria were measured on different scales, so factor 
values must be standardised before combination. The 
standardisation, typically, uses the minimum and maximum 
value as scaling points. This process uses a range of fuzzy set 
membership functions to convert the criteria values into 
another comparable scale, expressing a fuzzy standardised 
scale of values. 
The purpose of assigning weights to the criteria is to express 
the degree of importance of each factor in relation to others in 
the evaluation process, and it is a challenge step in the 
decision-making process. The relative weight of the 
vulnerability criterion is estimated by the Analytic Hierarchy 
Process (AHP) a method originally developed by Saaty [7]. 
The AHP compares criteria pair wise and then computes 
overall relative weights based on aggregate calculations of all 
pair wise ratios [8].  
The criteria weighting process was established by the 
judgments, according the following assumptions: (1) the 
Figure 2: Location of the apiaries. 
Figure 4: Legal constraints to beekeeping. 
 
 
Figure 3: The factors and constraints to potential beekeeping assessment. 
 
AGILE 2016 – Helsinki, June 14-17, 2016 
 
differentiation of the criteria relative importance considered 
by a panel of experts, and (2) the empirical knowledge of bee 
masters. In this case, Weighted Linear Combination (WLC) 
was used to combine the criteria and produce the potential 
beekeeping map. 
 
3 Results and discussion 
In the surrounding areas of urban areas and roads, apiaries 
can’t be installed due to legal framework. The constraints map 
obtained taking into account legal requirements is shown in 
Figure 4.  
After the criteria aggregation was possible the creation of a 
beekeeping potential map (Figure 5) with a continuous scale 
range classified into five equal interval classes. 
 
These results provide a framework, which may help policy 
makers, planners, beekeepers association, and bee masters to 
improve the beekeeping activity. 
In the study area 60% of the apiaries are located in high 
potential areas and the remaining apiaries should be relocated 
to improve the beekeeping activity. 
 
Acknowledgments 
This work is funded by project “ApiSIG - Modelação 
espacial do potencial de exploração apícola utilizando 
sistemas de informação geográfica, do Programa Apícola 
Nacional”. 
 
References 
[1] R. C .Estoque and Y. Murayama. Suitability analysis for 
beekeeping sites in La union, Philippines, using GIS 
&MCE techniques. Research Journal of Applied 
Sciences, 5, 242 – 253. 2010 
[2] S. C. Camargo, R.C. Garcia, F. A. Vasconcelos, E. S. 
Pires, B. G. Hartleben, A. M., Moraes, F J. Oliveira, G. J. 
Luan, E.S. Mittanck, J. R. Gremaschi, D. J. Pereira. 
Implementation of a geographic information system 
(GIS) for the planning of beekeeping in the west region 
of Paraná. Anais da Academia Brasileira de Ciências, 
86(2), 955-971. 2014 
[3] O. Anjos, G. Silva, N. Roque, P. Fernandez. GIS based 
analysis to support the beekeeping planning: In: 
Karačonji, I.B., Lušić, D. (Eds.), International 
Symposium on Bee Products 3rd edition – Annual 
meeting of the International Honey Commission (IHC), 
Opatija, Croatia. 2014 
[4] J. Malczewski. GIS-based multicriteria decision analysis: a 
survey of the literature. International Journal of 
Geographical Information Science, 20, 703-726. 2006. 
[5] T. Monteiro-Henriques. Landscape and phytosociology of 
the Paiva River’s hydrographical basin. Ph.D. thesis. 
Instituto Superior de Agronomia , TULisbon. Lisboa. 
2010 
[6] J. Malczewski. GIS and Multicriteria Decision Analysis. 
John Wiley & Sons Inc, New York. 1999. 
[7] T. Saaty. A scaling method for priorities in hierarchical 
structures. Journal of mathematical psychology, 15(3), 
234-281. 1977 
[8] D. Schmoldt, J. Kangas, G.A. Mendoza, M. Pesonen. The 
Analytic Hierarchy Process in natural resource and 
environmental decision making. Kluwer Academic 
Publishers, Dordrecht, Netherlands. 2001 
 
 
Figure 5: Map of beekeeping potential. 
 


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Spatial multicriteria decision analysis to potential beekeeping assessment. Case study: Montesinho Natural Park (Portugal)

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