Lúbia Vinhas

TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications

This chapter describes TerraLib, an open source GIS software library. The design goal for TerraLib is to support large-scale applications using socio-economic and environmental data. TerraLib supports coding of geographical applications using spatial databases, and stores data in different database management systems including MySQL and PostgreSQL. Its vector data model is upwards compliant with Open Geospatial Consortium (OGC) standards. It handles spatio-temporal data types (events, moving objects, cell spaces, modifiable objects) and allows spatial, temporal, and attribute queries on the database. TerraLib supports dynamic modeling in generalized cell spaces, has a direct runtime link with the R programming language for statistical analysis, and handles large image data sets. The library is developed in C++, and has programming interfaces in Java and Visual Basic. Using TerraLib, the Brazilian National Institute for Space Research (INPE) developed the TerraView open source GIS, which provides functions for data conversion, display, exploratory spatial data analysis, and spatial and non-spatial queries. Another noteworthy application is TerraAmazon, Brazil’s national database for monitoring deforestation in the Amazon rainforest, which manages more than 2 million complex polygons and 60 gigabytes of remote sensing images.

Free and Open Source GIS: Will There Ever Be a Geo-Linux?

This paper examines the constraints that limit the large-scale adoption of open source GIS. Although the open source GIS community has already achieved relevant results, their products have a small market share. There is equivalent to Linux and Apache in the open source GIS scene. We try to explain why this happens, by considering some factors that control the evolution and adoption of open source software. Our view is that the community effort is split in many different systems, not allowing a dominant solution to come forth. Thus, none of the current open source GIS has the potential to be a disruptive technology. Then, we consider a future scenario where most public geospatial data will be available as open access policy. This scenario is becoming more probable given recent data policy decision in Europe, USA and other countries. In this scenario, there is a major chance for a disruptive open source GIS to appear.

Fields as a Generic Data Type for Big Spatial Data

This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.

Geographical Information Engineering in the 21st Century

This paper discusses the challenges facing GIS designers in the 21st century. We argue that GI engineers lack a sound theoretical basis that would allow them to make best use of new technologies that handle geospatial data. Considering three important topics for the new generations of GIS (change, semantics, and cognition) we show that GIS theory is in a state of flux. Thus, researchers and engineers need to cooperate more for the new generation of GIS to be built in the best possible way.

Big earth observation data analytics: matching requirements to system architectures

Earth observation satellites produce petabytes of geospatial data. To manage large data sets, researchers need stable and efficient solutions that support their analytical tasks. Since the technology for big data handling is evolving rapidly, researchers find it hard to keep up with the new developments. To lower this burden, we argue that researchers should not have to convert their algorithms to specialised environments. Imposing a new API to researchers is counterproductive and slows down progress on big data analytics. This paper assesses the cost of research-friendliness, in a case where the researcher has developed an algorithm in the R language and wants to use the same code for big data analytics. We take an algorithm for remote sensing time series analysis on compare it use on map/reduce and on array database architectures. While the performance of the algorithm for big data sets is similar, organising image data for processing in Hadoop is more complicated and time-consuming than handling images in SciDB. Therefore, the combination of the array database SciDB and the R language offers an adequate support for researchers working on big Earth observation data analytics.

A mobile application for geographical data gathering and validation in fieldwork (invited talk)

Mobile devices, such as smartphones and tablets, are useful tools for in situ collecting information about spatial locations. In this paper, we describe the architecture of a mobile application for geographical data gathering and validation in fieldwork. This application is being developed based on well-established standards in order to assure spatial data interoperability between existing Spatial Data Infrastructures (SDI) and mobile systems.

Moving Objects and KML Files

Although KML files can be used to describe journeys, there is not a standard way to represent them as moving object trajectories for further analysis. In the KML schema, there is not a predefined element to describe a moving object trajectory. Each software or mobile device that generates KML files with trajectories uses its own structure for representing them. Therefore, this work proposes an interoperable way to extract moving object trajectories from any KML file, based on the processing of an additional metadata file. This metadata file is an XML that must be compliant with an XML schema proposed in this paper. This proposal has been implemented in a geographical software library as a proof of concept.

A spatiotemporal calculus for reasoning about land-use trajectories

ABSTRACT Earth observation images are a powerful source of data about changes in our planet. Given the magnitude of global environmental changes taking place, it is important that Earth Science researchers have access to spatiotemporal reasoning tools. One area of particular interest is land-use change. Using data obtained from images, researchers would like to express abstractions such as ‘land abandonment’, ‘forest regrowth’, and ‘agricultural intensification’. These abstractions are specific types of land-use trajectories, defined as multi-year paths from one land cover into another. Given this need, this paper introduces a spatiotemporal calculus for reasoning about land-use trajectories. Using Allen’s interval logic as a basis, we introduce new predicates that express cases of recurrence, conversion and evolution in land-use change. The proposed predicates are sufficient and necessary to express different kinds of land-use trajectories. Users can build expressions that describe how humans modify Earth’s terrestrial surface. In this way, scientists can better understand the environmental and economic effects of land-use change.