Spatial Interpolators to generate Population Density Surfaces in the Brazilian Amazon: problems and perspectives

1
Spatial Interpolators to generate Population
Density Surfaces in the Brazilian Amazon
problems and perspectives

• Silvana Amaral
• Antonio Miguel V. Monteiro
• Gilberto Câmara
• José A. Quintanilha

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Introduction

• Brazilian Amazonia 5 million km2, 4 million of
  forest
• Deforestation rate 15.787 km2/year
• Environment x Life quality
• Urban Population 1970 35.5, 2000 - 70
• Health, education and urban equipments -
  precarious
• Planning consider the human dimension
• POPULATION subject and object of the
  transformations ?

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Introduction

• Geographic phenomena computing representation
  models to socio-economic data
• Individual
• Area
• Continuous phenomena in space
• Area discrete region phenomena, homogenous unit
• Unit arbitrary as the census sector do NOT
  represent the spatial distribution of the
  variable.
• Modifiable Area Unit Problem (MAUP) temporal
  series???

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Introduction

• Surface Models alternatives to Area
  restrictions
• Demographic Density continuous phenomenon
• Objective to estimate distribution in detail (as
  better as possible)
• Advantage manipulation and analysis - Area
  independent
• Data storage and accessibility in Global Database
• Census Data Municipal boundaries or census
  sector
• Land use and coverage evolution in Amazonia
• Territorial divisions
• Regular grid for spatial models
• Population pressure Population density gradient

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Introduction

• Objective discuss the principal spatial
  interpolation techniques used to represent
  Population at density surfaces and indicate the
  more suitable methods to represent population in
  the Amazonia Region.

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To represent Population in Amazonia

• Data availability
• Census Data (10 years)
• Inter-census counting based on sampling
• Statistic estimates PNAD UF, metropolitan
  region, only for urban population in the N
  region
• Spatial Reference
• Municipal limits up to 2000 census, (analogical
  maps), official territorial limit (IBGE)
  municipal
• 2000 census digital census sector (just to the
  urban area mun. gt 25,000 inhabitants)

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To represent Population in Amazonia

• Census Zone
• Surveyed area - 1 month 350 rural residences
  250 urban
• Amazonia vast areas and heterogeneous
• Alta Floresta dOeste (RO)

• 165 km2 and regular boundaries settlements
• 435 km2 in forested areas

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To represent Population in Amazonia

• Region Heterogeneity
• Municipal Dimension Raposa (MA) - 64 km2,
  Altamira (PA)
  160,000 km2
• Municipal Area Average 6,770 km2, Stand.
  Dev.14,000 km2
• RO 52 municipios average area of 4,600 km2
• AM - 62 municipios average area of 25,800 km2
• Municipal area influences the census zone
  dimension

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To represent Population in Amazonia

• Process complexity -gt spatial distribution
• Rondônia migrants, INCRA settlements, urban
  nuclei along the road axis and population at
  rural zone.
• Amazonas lower urban nuclei density,
  concentrated in Manaus.
• Tendencies
• Dispersion from metropolis,
• Increasing relative participation of cities up to
  100,000 inhab.
• Population growing at 20,000 inhab. nuclei
• Dispersal population at rural zone and along
  river sides
• Forest continuous demographic emptiness

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Population Models

• Human Dispersion Important at regional projects
  - LBA and LUCC
• More frequent representation Thematic Maps

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Population Models

• Demographic Density instead of Total Population
  2000
• Visualization Intervals and criteria
• Highlight Densely populated regions and
  Demographic emptiness

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Population Models

• Surface Interpolation Techniques - Models two
  groups
• Considering only one variable POPULATION
• Area Weighted, Kriging, Tobler Pycnophylatic,
  Martins Population Centroids
• Considering auxiliary variables, human presence
  indicators
• Dasimetric method, Intelligent Interpolators and
  variants

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Univariate Population Models

• Area Weighted
• Population Density proportional to the
  intersection between original zones and grid
  cells.
• Sharp limits in the boundaries and constant
  values inside the units.
• Error increases with
• more clustered distribution,
• smaller destiny regions compared to the origin
  regions
• At the Amazonia region gt raster representation
  of the Population Density (previous map)

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Univariate Population Models

• Kriging
• Interpolation for spatial random process. It
  estimates the occurrence of an event in a certain
  place based on the occurrence in other places.
• The variable values are dependent of the distance
  between them, a function describes this spatial
  distribution.
• Using Municipal centres as sample points, taking
  the demographic density (log) gt a gaussian
  function can model the population spatial
  distribution

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Spatial Representation - Univariate

• Kriging
• Imprecision for modeling
• Population volume
• Empty areas
• Synoptic vision
• General Tendency

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Univariate Population Models

• Tobler Pycnophylatic
• Based on the Geometric centroids of the census
  unit
• Smooth surface average filter

• Weighted by the centroid distance, concentric
  demographic density function
• Population value for the entirely surface (there
  is NO zeros)
• Consider the adjacent values and maintain the
  Population volume

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Univariate Population Models

• Tobler Pycnophylatic
• Ex Global Demography Project, 9km grid, 1994.
• Municipal Data
• Homogeneous region, diffuse boundaries
• RO smaller municipios, interpolator effect.
• Better results smaller units (census zone) and
  high populated areas.

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Univariate Population Models

• Based on Kernel

• Martins Centroids Weighted Census mapping - UK
• Adaptive Kernel point density define the
  populated area extension
• Distance decay function
• Weight for each cell redistribute the total
  counting
• Function shape affects the distribution of the
  population over areas

• Rebuild the distribution geography, maintaining
  areas without population at the final surface.

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Univariate Population Models

• Kernel 2000
• Municipal centres - centroids
• Gradient at high populated areas
• Demographic emptiness preserved
• Better results additional centroids (districts
  and RS images), and smaller units and densely
  populated regions

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Multivariate Population Models

• Auxiliary variables - human presence indicators -
  to distribute population
• Dasimetric Method Remote Sensing classified
  images weights to disaggregate
• Intelligent Interpolators Spatial information
  from other sources to guide the interpolation
• A weighted surface map the original data on the
  final surface
• Predictors variables x interest variables

Land use categories
High housing
Low housing
Industry
Open space
Probabilities by raster cell detail
Weights
10
5
1
1
n total weights of zone
No intervals
Probability
Zonal data to microdata
100 50 10 Data element
1483 Data element
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Multivariate Population Models

• Intelligent Interpolators
• Ex LandScan 1km grid, 1995
• Population Model land use, roads proximity,
  night-time lights gt probability coefficients
• Population at risk information for emergency
  response for natural disasters or anthropogenic

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Multivariate Population Models

• Intelligent Interpolators - Variants
• Clever SIM besides the auxiliary variables,
  neural network to
• understand the relations between predictors
  variables and population
• generate the surface.
• Crucial variable selection and interactions
  model
• Availability and quality of the auxiliary data -gt
  responsible for the final density surface
  precision

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Perspectives

• Density Surfaces in Amazonia
• Interpolator Methods characteristics e
  restrictions
• Adaptive Approach based on scale of analysis
  and phenomena complexity
• Scaling Top-Down
• Amazonia Legal
• Multivariate models heterogeneities
• Univariate Models Tobler related to the
  sampling unit Martin additional centroids
  Kriging general tendencies gtOK
• Kriging including barriers (further)

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Perspectives

• Macro-zones Spatial-Temporal Subdivision
• I. Oriental and South Amazonia
  deforestation arc
• Martins Centroids Weighted villages, districts,
  night-time lights
• II. Central Amazonia Pará, new axis region
• Multivariate Model - intelligent Interpolators
• Scenarios Analyze as BR-163 paving
• III. Occidental Amazonia Nature rhythm
• Multivariate Model Disaggregating by land use
  (e.g.)

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Finally

• Scale Census Zones
• Tobler Pycnophylatic or Martins Centroids
  Weighted
• The interpolation procedure should be defined
  according to the analysis of land use and
  settlement process in the region different
  characteristics considering capital, frontier,
  ranching, etc.
• To be continued
• Define and execute an experimental procedure to
  generate population density surface for the
  Amazonia region, following the approach proposed,
  with data validation and analysis of results.

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Some results

• Population Density Surface - Kriging

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Some results

• Population Density Surface - Kriging