Artificial neural network for ecological-economic zoning as a tool for spatial planning

Sadeck, Luis Waldyr Rodrigues; Lima, Aline Maria Meiguins de; Adami, Marcos

doi:10.1590/S0100-204X2017001100011

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Pesquisa Agropecuária Brasileira

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Sumário

REMOTE SENSING • Pesq. agropec. bras. 52 (11) • Nov 2017 • https://doi.org/10.1590/S0100-204X2017001100011 copy

Artificial neural network for ecological-economic zoning as a tool for spatial planning

Rede neural artificial para o zoneamento ecológico-econômico como instrumento de ordenamento territorial

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract:

The objective of this work was to analyze social and environmental information through an artificial neural network-self-organizing map (ANN-SOM), in order to provide subsidy to ecological-economic zoning (EEZ) as a tool to reduce the subjectivity of the process. The study area comprises 16 municipalities in the northeast of the state of Pará, Brazil, representative of the agricultural development in the state. Data processing involved three steps: preparation of the data in a geographic information system (GIS) environment; mathematical processing (ANN-SOM) of the data; and visualization and interpretation of the processing results, allowing the spatial planning of northeastern Pará. The results comprised 13 classes, regrouped according to behavioral similarity criteria into four categories, which represent the main areas of sustainability proposed for the state of Pará, according to existing EEZ. The proposed methodology allows individualizing areas in the region that EEZ had not defined, mainly due to the greater possibility of combining and integrating a large number of physical, social, and economic variables through the SOM.

Index terms:
Amazon; regional planning; regionalization; self-organizing maps

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[TFN1] ⁽¹⁾shp, shapefile; and xlsx, Excel spreadsheet.

	Material	Scale	File type⁽¹⁾	Source
Use and coverage 2010	Vector basis of land use and cover	1:100 000	shp	CRA (2014)CRA. Centro Regional da Amazônia. Dados TerraClass. Available at: ˂Available at: ˂http://www.inpe.br/cra/projetos_pesquisas/dados_terraclass.php ˃. Accessed on: Oct. 16 2014. http://www.inpe.br/cra/projetos_pesquisa...
Socioeconomic data 2010	Vector and numerical data associated with socioeconomic information (population, economic activities, territorial boundaries...)	Municipality	xlsx and shp	IBGE (2014b)IBGE. Instituto Brasileiro de Geografia e Estatística. Sistema IBGE de Recuperação Automática - SIDRA. Available at: <Available at: http://www.sidra.ibge.gov.br/bda/tabela/protabl.asp?c=1613&z=p&o=28&i=P >. Accessed on: Oct. 12 2014b. http://www.sidra.ibge.gov.br/bda/tabela/... and PNUD (2014)PNUD. Programa das Nações Unidas para o Desenvolvimento no Brasil. Available at: ˂Available at: ˂http://www.pnud.org.br /˃. Accessed on: Oct. 12 2014. http://www.pnud.org.br...
Environmental data	soil (psolo)	1:250 000	shp	Brasil (2014)BRASIL. Ministério do Meio Ambiente. Download de dados geográficos. Available at: Available at: http://mapas.mma.gov.br/i3geo/datadownload.htm ˃. Accessed on: Oct. 12 2014. http://mapas.mma.gov.br/i3geo/datadownlo...
	Rainfall (pclima)	Jan./Dec.	shp	NASA (2014)NASA. National Aeronautics and Space Administration. Precipitation Measurement Missions. TRMM Data Downloads. Available at: ˂Available at: ˂https://pmm.nasa.gov/data-access/downloads/trmm ˃. Accessed on: Oct. 6 2014. https://pmm.nasa.gov/data-access/downloa...
	Geological units (pgeo)	1:250 000	shp	Brasil (2014)BRASIL. Ministério do Meio Ambiente. Download de dados geográficos. Available at: Available at: http://mapas.mma.gov.br/i3geo/datadownload.htm ˃. Accessed on: Oct. 12 2014. http://mapas.mma.gov.br/i3geo/datadownlo...
	Cover vegetation (pveg)	1:250 000	shp	Brasil (2014)BRASIL. Ministério do Meio Ambiente. Download de dados geográficos. Available at: Available at: http://mapas.mma.gov.br/i3geo/datadownload.htm ˃. Accessed on: Oct. 12 2014. http://mapas.mma.gov.br/i3geo/datadownlo...
	Geomorphological units (pgeom)	1:250 000	shp	Brasil (2014)BRASIL. Ministério do Meio Ambiente. Download de dados geográficos. Available at: Available at: http://mapas.mma.gov.br/i3geo/datadownload.htm ˃. Accessed on: Oct. 12 2014. http://mapas.mma.gov.br/i3geo/datadownlo...
PRODES 2010	Information on deforestation in the Amazon	1:250 000	shp	OBT (2014)OBT. Coordenação-Geral de Observação da Terra. Projeto PRODES: monitoramento da Floresta Amazônica brasileira por satélite. Available at: ˂http://www.obt.inpe.br/prodes/index.php˃. Accessed on: Oct. 6 2014. http://www.obt.inpe.br/prodes/index.php...
Cartographic base	Drainage network, access roads, municipal offices	1:250 000	shp	IBGE (2014a)IBGE. Instituto Brasileiro de Geografia e Estatística. Downloads: geociências. Available at: http://downloads.ibge.gov.br/downloads_geociencias.htm. Accessed on: Oct. 6 2014a. http://downloads.ibge.gov.br/downloads_g...
PostgreSQL/PostGIS	Spatial database manager	-	-	PostgreSQL (2014)POSTGRESQL GLOBAL DEVELOPMENT GROUP. PostgreSQL. Available at: ˂Available at: ˂https://www.postgresql.org/download/windows /˃. Accessed on: Oct. 6 2014. https://www.postgresql.org/download/wind...
TerraView 4.2.2	Georeferencing and spatial analysis	-	-	INPE (2014)INPE. Instituto Nacional de Pesquisas Espaciais. TerraView. 4.2.2. São José dos Campos, [2014].
MatLAB	Information processing	-	-	MATLAB (2014)MATLAB. Available at: ˂Available at: ˂https://www.mathworks.com/products/matlab.html ˃. Accessed on: Oct. 12 2014. https://www.mathworks.com/products/matla...
SOM Toolbox 2.0	Information processing	-	-	Helsinki University of Technology (2014)HELSINKI UNIVERSITY OF TECHNOLOGY. Laboratory of Computer and Information Science. [SOM Toolbox 2.0]. Available at: ˂Available at: ˂http://www.cis.hut.fi/somtoolbox /˃. Accessed on: Nov. 10 2014. http://www.cis.hut.fi/somtoolbox...

Class	Characteristics
Class 0	Composed mainly by areas not observed due to clouds.
Class 1	High vulnerability to vegetation cover, geomorphology, precipitation, and geology. “Várzea” (floodplain) areas with secondary vegetation and abandoned pastures with a high degree of regeneration, being concentrated in municipalities with a more rural population and with low potential for socioeconomic development.
Class 2	Areas with higher population density (in urban areas) and urban infrastructure, with better education, health and income, showing the lowest rates of social inequality. Agricultural activity is quite developed and there is a reasonable production-flow infrastructure.
Class 3	Mainly conditioned by the behavior of the variables geology and soil; the latter is determinant for the creation of this grouping. This class does not present great socioeconomic potential, and uses in the region aim at regeneration of abandoned pastures, reforestation areas, floodplains with secondary vegetation, among others.
Class 4	Defined by a high rural population density, associated with variable precipitation and varied uses. It has moderately stable characteristics regarding soil loss, low production runoff infrastructure, and very low human development.
Class 5	These are floodplain areas, with units of use between reforestation, regeneration with pasture, secondary vegetation, and others, totaling approximately 85% of the area. Its socioeconomic potential is quite low, as well as its population density.
Class 6	High vulnerability, with a predominantly rural population and low potential for human development and urban infrastructure. Modulated by natural variables (geomorphology, precipitation) and by population density (rural population).
Class 7	It has a high population density, with well-developed socioeconomics and dense infrastructure, having better teaching, health and income qualities, resulting in lower rates of social inequality.
Class 8	High potential for human development with a good production infrastructure and low environmental vulnerability, but the population of the region is much smaller.
Class 9	Defined by natural variables (geology, soil, and geomorphology) and urban population, indicating a high environmental vulnerability. It is a very developed region, has ample infrastructure, and land use is divided between pasture areas and secondary vegetation.
Class 10	The most vulnerable among the 12 classes. It is located in lowland areas and conditioned by natural variables (soil, geomorphology, and precipitation). Presents high environmental vulnerability and low socioeconomic potential.
Classes 11 and 12	They exhibit intermediate behavior. In class 11, socioeconomic issues and infrastructure are more determinant for the formation of the grouping. The environmental variables geology and precipitation indicate high vulnerability. In class 12, almost 67% of the area is related to some type of pasture.

Item	Conventional	ANN-SOM⁽¹⁾
Data base	Preferably matrix, but can also use vector data.	It works with tabular data that can be derived from the table of the added vector.
Normalization	The data ranges from 1 to 3 and the environmental data are subdivided into 21 unique weight intervals that are subsequently divided into 5 groups; socioeconomic data ranges from 1 to 3.	All data, environmental and socioeconomic, are normalized from 0 to 1, respecting the given weights.
Processing	It is supervised step-by-step. The team needs to check each intermediate result up to the synthesis map.	It is not supervised, simply presenting the normalized data to the network so that the mathematical processing is executed and the class map (grouping) is generated.
Technique	Linear operations appropriate to the input data may cause errors in the demarcation of the output classes.	Nonlinear operations containing a large number of associated processing elements that learn to make decisions even with imperfect data, being tolerant of failures and other problems that may occur.
Adjustment	Adjustments are made at each step. Briefly, the steps are generating the following maps: map of vulnerabilities; potential map; territory sustainability map; and summary map of territorial management. If one of these steps requires adjustment, it is necessary to return to the lower level of the process to do so.	Once the ANN-SOM is configured, the settings are generally done only in the input data. After setting, the network can be run again.
Synthesis map	After all the processing and adjustment stages and the participatory workshops, the synthesis map is generated, which is based on socio-environmental issues, as well as on political and cultural ones.	The synthesis map is generated based on the socioeconomic and environmental interpretation of the classes generated by SOM, and nothing prevents this map from being discussed by the local community.