1 / 27

Population Density Surfaces in Brazilian Amazon: Problems and Perspectives

This article discusses spatial interpolation techniques used to generate population density surfaces in the Brazilian Amazon region and identifies the most suitable methods for representing population in this area.

ahenkel
Download Presentation

Population Density Surfaces in Brazilian Amazon: Problems and Perspectives

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

  2. 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 ?

  3. 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???

  4. 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

  5. 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.

  6. 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)

  7. 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

  8. 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

  9. 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

  10. Population Models • Human Dispersion: Important at regional projects - LBA and LUCC • More frequent representation: Thematic Maps

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

  12. 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

  13. “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)

  14. “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

  15. Manaus -> Pará RO Spatial Representation - “Univariate” • Kriging • Imprecision for modeling • Population volume • Empty areas • Synoptic vision • General Tendency

  16. “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

  17. Manaus -> Pará RO “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.

  18. “Univariate” Population Models • Based on Kernel • 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.

  19. “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

  20. “Multivariate” Population Models Land use categories • 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 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

  21. “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

  22. “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

  23. 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 • Krigingincluding barriers (further)

  24. 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.)

  25. 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.

  26. Some results Population Density Surface - Kriging

  27. Some results Population Density Surface - Kriging

More Related