As instituições administrativas e o planejamento territorial paulista: 1938-1967

O objetivo é analisar o papel das instituições administrativas no planejamento urbano e regional do Estado de São Paulo. Nosso objeto de estudo são as leis e decretos estaduais e federais e as ações do Estado que definiram os padrões de organização territorial das cidades. Entendemos que a partir deles as ações administrativas atribuíram às cidades um modelo de planejamento setorizado e polarizado. Entre as décadas de 1930 e 1960, esse modelo formou a base para a aplicação de uma divisão funcional urbana fundada nas características produtivas e responsável pela provisão de recursos. A partir da década de 1960, a organização territorial paulista foi padronizada pelo conceito de polo urbano e o planejamento urbano e regional ficou submetido às condicionantes econômicas.

The participation of the nutritionist in Primary Health Care in a large urban center

Nutritionists are important professionals for ensuring the implementation of health promotion, treatment and rehabilitation. However, their participation in primary healthcare from a quantitative standpoint is limited. The city of Sao Paulo has experienced an uneven urbanization process triggering new problems of insecurity in terms of food and nutrition. This article analyzes the performance of the primary healthcare nutritionist in a large urban center. It is a quantitative study that used data from the Municipal Health Department, population data of Sao Paulo and a semi-structured questionnaire applied in individual interviews. All regions of the city are found to have fewer nutritionists than the recommendation of the Federal Council of Nutritionists. There are 123 nutritionists in the basic healthcare network and 51 in the Family Health Support Nuclei (FHSN) (57.3%). Each nutritionist from the FHSN accompanies 7.1 family health strategy teams on average. The age groups corresponding to children are less frequently seen by nutritionists. Comparing the activities, the transition from a model of primary health care focused on individual care to a model that prioritizes group care was observed.

STATE INSTITUTIONS, POWER, AND SOCIAL NETWORKS IN BRAZILIAN URBAN POLICIES

A large historiographic tradition has studied the Brazilian state, yet we know relatively little about its internal dynamics and particularities. The role of informal, personal, and unintentional ties has remained underexplored in most policy network studies, mainly because of the pluralist origin of that tradition. It is possible to use network analysis to expand this knowledge by developing mesolevel analysis of those processes. This article proposes an analytical framework for studying networks inside policy communities. This framework considers the stable and resilient patterns that characterize state institutions, especially in contexts of low institutionalization, particularly those found in Latin America and Brazil. The article builds on research on urban policies in Brazil to suggest that networks made of institutional and personal ties structure state organizations internally and insert them,into broader political scenarios. These networks, which I call state fabric, frame politics, influence public policies, and introduce more stability and predictability than the majority of the literature usually considers. They also form a specific power resource-positional power, associated with the positions that political actors occupy-that influences politics inside and around the state.

Ground-level ozone prediction using a neural network model based on meteorological variables and applied to the metropolitan area of Sao Paulo

A neural network model to predict ozone concentration in the Sao Paulo Metropolitan Area was developed, based on average values of meteorological variables in the morning (8:00-12:00 hr) and afternoon (13:00-17: 00 hr) periods. Outputs are the maximum and average ozone concentrations in the afternoon (12:00-17:00 hr). The correlation coefficient between computed and measured values was 0.82 and 0.88 for the maximum and average ozone concentration, respectively. The model presented good performance as a prediction tool for the maximum ozone concentration. For prediction periods from 1 to 5 days 0 to 23% failures (95% confidence) were obtained.