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

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 ecologicaleconomic 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. Rede neural artificial para o zoneamento ecológico-econômico como instrumento de ordenamento territorialResumo -O objetivo deste trabalho foi analisar informações socioambientais por meio de rede neural artificial-mapa auto-organizável (RNA-SOM), para fornecer subsídio ao zoneamento ecológico econômico (ZEE) como instrumento para diminuir a subjetividade do processo. A área de estudo compreende 16 municípios do Nordeste Paraense, expressivos no desenvolvimento agropecuário do estado. O tratamento dos dados envolveu três etapas: preparação dos dados em ambiente de sistema de informação geográfica (SIG); processamento matemático (RNA-SOM) dos dados; e visualização e interpretação dos resultados dos processamentos, o que permitiu o ordenamento territorial do Nordeste Paraense. Os resultados compreenderam 13 classes, reagrupadas de acordo com critérios de similaridade de comportamento em quatro categorias, que representam os principais eixos de sustentabilidade propostos para o Estado do Pará, a partir do ZEE existente. A metodologia proposta permite individualizar zonas na região que o ZEE não havia definido, principalmente em razão da maior possibilidade de conjugar e integrar um grande número de variáveis físicas, sociais e econômicas por meio do SOM.Termos para indexação: Amazônia, planejamento regional, regionalização, mapas auto-organizáveis.

Combining ecological knowledge with Brazilian urban zoning planning

“…To classify zones in cities, besides consider ecological information, it is necessary include social and economic variables in urban zoning (Freitas & Santos, 2014;Sadeck et al, 2017). The inclusion of this information can be done through data spatialization, with geotechnologies attached to landscape ecology that assist areas classification (Freitas & Santos, 2014).…”

“…The inclusion of this information can be done through data spatialization, with geotechnologies attached to landscape ecology that assist areas classification (Freitas & Santos, 2014). With an integrated data base (environmental, social and economic information), urban landscape can be categorized into four interest classes: consolidation, expansion, recovery and conservation (Sadeck et al, 2017). Zone categories in cities protects environmental interests (e.g.…”

Automatic Segmentation of the Self-organizing Map to Support Territorial Zoning

“…In the artificial neural network class of models, the Self-Organizing Map (SOM) excels at preserving statistical properties from the dataset and allows visualization of high-dimensional input patterns. SOM's recent use in territorial zoning applications is increasing, such as in [Silva et al 2018] and [Sadeck et al 2022].…”