Post on 12-Jan-2016
Spatial Interpolators to generate Population Density Surfaces in the Brazilian Amazon: problems and
perspectives
Silvana AmaralAntonio Miguel V. Monteiro
Gilberto CâmaraJosé A. Quintanilha
GEOINFO – Dez/2002
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 ?
GEOINFO – Dez/2002
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???
GEOINFO – Dez/2002
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
GEOINFO – Dez/2002
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.
GEOINFO – Dez/2002
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. > 25,000 inhabitants)
GEOINFO – Dez/2002
To represent Population in Amazonia…
Census Zone Surveyed area - 1 month:
350 rural residences 250 urban
Amazonia – vast areas and heterogeneous
Alta Floresta d’Oeste (RO)
165 km2 and regular boundaries –settlements
435 km2 in forested areas
GEOINFO – Dez/2002
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
GEOINFO – Dez/2002
To represent Population in Amazonia…
Process complexity -> 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
GEOINFO – Dez/2002
Population Models
Human Dispersion: Important at regional projects - LBA and LUCC
More frequent representation: Thematic Maps
GEOINFO – Dez/2002
Population Models
Demographic Density instead of Total Population 2000
Visualization: Intervals and criteria
Highlight: Densely populated regions and Demographic emptiness
GEOINFO – Dez/2002
Population Models
Surface Interpolation Techniques - “Models” – two groups: Considering only one variable – POPULATION:
Area Weighted, Kriging, Tobler Pycnophylatic, Martin’s Population Centroids
Considering auxiliary variables, human presence indicators:
Dasimetric method, Intelligent Interpolators and variants
GEOINFO – Dez/2002
“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 –> raster representation of the Population Density (previous map)
GEOINFO – Dez/2002
“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) –> a gaussian function can model the population spatial distribution
GEOINFO – Dez/2002
Spatial Representation - “Univariate”
KrigingImprecision for modeling
Population volume
Empty areas Synoptic vision General
Tendency
Manaus ->
RO
Pará
GEOINFO – Dez/2002
“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
GEOINFO – Dez/2002
“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.
Manaus ->
RO
Pará
GEOINFO – Dez/2002
“Univariate” Population Models
Martin’s 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.
Based on Kernel
GEOINFO – Dez/2002
“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
GEOINFO – Dez/2002
“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
Probability No intervals
Weights
10
5
1
1
n total weights of zone
Land use categories
High housingLow housing
Industry
Open space
Probabilities by raster cell detail
Zonal data to microdata100 5010 Data element
1483Data
element
GEOINFO – Dez/2002
“Multivariate” Population Models
Intelligent Interpolators : Ex: LandScan –1km grid,
1995
Population Model: land use, roads proximity, night-time lights => probability coefficients
Population at risk: information for emergency response for natural disasters or anthropogenic
GEOINFO – Dez/2002
“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 -> responsible for the final density surface precision
GEOINFO – Dez/2002
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 =>OK
Kriging including barriers (further)
GEOINFO – Dez/2002
Perspectives
Macro-zones: Spatial-Temporal Subdivision:
I. Oriental and South Amazonia: “deforestation arc”
Martin’s 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.)
GEOINFO – Dez/2002
Finally
Scale – Census Zones
Tobler Pycnophylatic or Martin’s 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.