SearcharxivSearch

arXiv · 1910.03458

Simulation of land use dynamics in Paragominas-PA: differences in spatial rules between smallholdings and agribusiness areas

Abstract

The aim of this paper is to present the results of the land use dynamic simulations in the municipality of Paragominas-PA. The simulation is based on models built from past land use data and spatial variables of the natural environment and infrastructure. Two spatial units were analyzed: the central area of commercial agricultural production and the area of settlements and smallholdings the east. The results show distinct spatial dynamics between the analyzed areas, among which we highlight the role of soil characteristics and, associated with the topography and the occupation history, are part of the context in which is defined rationality producers. Considering the transition from forest to pasture in the commercial farming area most often associated soils are sandy. This raises the following hypothesis: the deforestation that occurred in the period are related to livestock activities. Livestock favors access to water and low fertility sands does not affect production. On the other hand, the soybean expansion occurred preferentially on existing pastures and on clay soils (Belterra clay), reducing the availability of pastures on these soils. The relative importance of types of soil increases with time. In the area of settlements, the transition from forest to pasture and family crops occurred preferentially on the variegated clay. However, it is possible that the prevalence of this transition on this texture has been given due more to the history of occupation of this area. Historically the sandy valleys were the first to be occupied, and the continuity of the deforestation occurred toward the slopes dominated by variegated clay and plateaus with Belterra clay. These associations observed indicate that, within a wider context of social, economic and political factors, natural variable factors in space are important for the choice of managements in the properties, but they are done differently in the territory, and the best knowledge of these relationships are useful for territorial planning.

Explore related subjects

Keep this discovery

BibTeXRIS

Reinis Osis, François Laurent, René Poccard-Chapuis. 2019-10-08. Simulation of land use dynamics in Paragominas-PA: differences in spatial rules between smallholdings and agribusiness areas. https://arxiv.org/abs/1910.03458

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Privacy-Preserving Causal Meta-Mediation Analysis with Survival Outcomes

Privacy and data-governance constraints often prevent pooling individual-level data across studies, limiting the use of conventional approaches for causal media- tion analysis in multicenter settings. We propose a federated causal meta-mediation framework for right-censored time-to-event outcomes that enables collaborative es- timation without sharing individual-level data. Our framework targets natural indirect effects in a prespecified population by combining information on mediator and outcome mechanisms across distributed data sources. A site-by-site identifi- cation strategy further allows heterogeneity across data sources to be character- ized, with a variance decomposition separating outcome-related, mediator-related, and interaction components. We develop federated one-step and targeted maxi- mum likelihood estimators that accommodate data-adaptive and machine-learning methods for nuisance-function estimation. The finite-sample performance of the proposed estimators is evaluated through numerical simulations. To illustrate the practical utility of the framework, we apply it on data from the French National Health Data System to evaluate the role of methotrexate coprescription in explain- ing the effect of TNFi versus IL-12/23 inhibitor therapy on treatment persistence among psoriatic patients.

stat.AP

Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices

Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect health. We evaluated whether numerical representations of physical place produced by four geospatial foundation model families from 2022 satellite data explained residual variance in tract-level associations between the Area Deprivation Index, Social Deprivation Index, and Social Vulnerability Index with health outcomes. We used LightGBM to predict variables from the American Community Survey and 40 chronic disease and health-behavior outcomes from CDC PLACES across 82,646 census tracts in the contiguous United States, evaluating performance across 10 held-out states. Among survey variables, models were moderately predictive of some variables including housing type (R-squared up to 0.54) but weak for disability, unemployment, and income disparity. For health outcomes, models explained up to 54% of variance left unexplained by social risk indices, with the largest gains for annual checkups, arthritis, and high blood pressure. Mean total variance explained by geospatial foundation models across the 40 health-related outcomes increased from 0.31 in the smallest tract-size decile to 0.39 in the largest. Geospatial foundation models capture health-relevant features of place not represented by conventional social risk indices and may usefully augment them in epidemiological analyses.

stat.AP

A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections

Tuberculosis (TB) remains a critical public health concern in Brazil, characterized by pronounced spatial heterogeneity and fluctuating temporal volatility. In this paper, we study monthly TB notifications across 61 microregions of Sao Paulo state from 2001 to 2024. To do this, we introduce a negative binomial spatial integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) model featuring jointly dynamic conditional means and time-varying dispersion. To capture inter-regional spillovers, we incorporate both discrete adjacency structures and a novel continuous distance-based formulation leveraging the Matern correlation function. Parameter estimation via conditional maximum likelihood employs a two-step profile-likelihood iterative scheme, demonstrating solid finite-sample performance in simulation studies. Applied to the Sao Paulo TB surveillance data, the framework substantially outperforms standard Poisson and fixed-dispersion spatiotemporal baselines in empirical fit and uncertainty quantification, maintaining nominal 95% predictive coverage across both dense metropolitan centers and rural microregions. Our results reveal marked spatial heterogeneity in baseline incidence, dynamic overdispersion driven by localized outbreaks, and short-range spatial interaction decay. By accurately modeling spatiotemporal volatility, the proposed methodology provides a robust statistical tool to support public health surveillance, policy-making, and resource allocation.

stat.AP