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???

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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

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

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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, 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

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Spatial Representation - “Univariate”

KrigingImprecision for modeling

Population volume

Empty areas Synoptic vision General

Tendency

Manaus ->

RO

Pará

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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

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

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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

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

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“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

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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 -> 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 =>OK

Kriging including barriers (further)

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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.)

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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.

GEOINFO – Dez/2002

Some results

Population Density Surface - Kriging

GEOINFO – Dez/2002

Some results

Population Density Surface - Kriging