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

GEOINFO – Dez/2002 Introduction Brazilian Amazonia – 5 million km 2, 4 million of forest Deforestation rate km 2 /year Environment x Life quality Urban Population 1970 – 35.5%, % 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 km 2 and regular boundaries – settlements 435 km 2 in forested areas

GEOINFO – Dez/2002 To represent Population in Amazonia… Region Heterogeneity Municipal Dimension: Raposa (MA) - 64 km 2, Altamira (PA) – 160,000 km 2 Municipal Area: Average = 6,770 km 2, Stand. Dev.=14,000 km 2 RO – 52 municipios – average area of 4,600 km 2 AM - 62 municipios – average area of 25,800 km 2 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” Kriging Imprecision 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, 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 n total weights of zone Land use categories High housing Low housing Industry Open space Probabilities by raster cell detail Zonal data to microdata Data element 1483 Data 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.

GEOINFO – Dez/2002 Some results Population Density Surface - Kriging

GEOINFO – Dez/2002 Some results Population Density Surface - Kriging