Conclusions
This paper proposed a methodology to disaggregate population data provided in census tracts into smaller spatial units based on ancillary environmental data and geoinformation techniques. The results show that it is possible to recover the heterogeneity of the census tracts whenever the relations between the indicator variables and population occurrence are defined with criterion and the local particularities are taken into account. The methodology developed for the municipality of Marabá was adapted to the Sustainable Forest District of BR-163 municipalities. As the area of interest was expanded, the cell size was enlarged, and the pattern of population distribution was obtained from the presence of communities. Data from fieldwork indicated an adequate fit between the population count predicted from the population surface and the total population for the communities along BR-163 highway.
The population density surfaces enabled the interpretation of the distribution of human presence in terms of the territory to be potentially occupied. The model allocates no population in areas where there is no possibility of human presence, such as in rivers, dense forests cover, sand islands, etc. Moreover, representing population in cell spaces enables monitoring of the population over the time. Even if the limits of municipalities or census tracts change, what is very common in such dynamic regions as Amazon, the distribution can be represented and compared in a cell space.
The evolution of the resident population over the DFS/BR-163 territory from 2000 to 2007 showed spatial patterns comparable to the occupation process described in the literature and reported in the field. Therefore,
(a)
(b)
17
since the proposed methodology can be adapted to represent the population distribution of other areas, population density surfaces can be useful as additional data source to study population and territory dynamics.
The proposed methodology can be improved using knowledge about the spatial indicator variables and human presence relationships. With population data from the 2010 census, we will be able to represent the population density evolution over a ten-year period and better monitor the impacts of the creation of a sustainable forest district on the population distribution in the region of BR-163 highway.
Conclusions
This paper proposed a methodology to disaggregate population data provided in census tracts into smaller spatial units based on ancillary environmental data and geoinformation techniques. The results show that it is possible to recover the heterogeneity of the census tracts whenever the relations between the indicator variables and population occurrence are defined with criterion and the local particularities are taken into account. The methodology developed for the municipality of Marabá was adapted to the Sustainable Forest District of BR-163 municipalities. As the area of interest was expanded, the cell size was enlarged, and the pattern of population distribution was obtained from the presence of communities. Data from fieldwork indicated an adequate fit between the population count predicted from the population surface and the total population for the communities along BR-163 highway.
The population density surfaces enabled the interpretation of the distribution of human presence in terms of the territory to be potentially occupied. The model allocates no population in areas where there is no possibility of human presence, such as in rivers, dense forests cover, sand islands, etc. Moreover, representing population in cell spaces enables monitoring of the population over the time. Even if the limits of municipalities or census tracts change, what is very common in such dynamic regions as Amazon, the distribution can be represented and compared in a cell space.
The evolution of the resident population over the DFS/BR-163 territory from 2000 to 2007 showed spatial patterns comparable to the occupation process described in the literature and reported in the field. Therefore,
(a)
(b)
17
since the proposed methodology can be adapted to represent the population distribution of other areas, population density surfaces can be useful as additional data source to study population and territory dynamics.
The proposed methodology can be improved using knowledge about the spatial indicator variables and human presence relationships. With population data from the 2010 census, we will be able to represent the population density evolution over a ten-year period and better monitor the impacts of the creation of a sustainable forest district on the population distribution in the region of BR-163 highway.
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