Space-time analytics of human physiology for urban planning.

Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences

The future is distributed: a vision of sustainable economies

“The Future is distributed: a vision of sustainable economies” is a collection of case studies on distributed economies, a concept describing sustainable alternatives to the existing business models. The authors of this publication are international Masters students of the Environmental Sciences, Policy and Management Programme at the International Institute for Industrial Environmental Economics at Lund University in Sweden. The aim of their work is to demonstrate that local, small-scale, community-based economies are not just part of the theory, but have already been implemented in various sectors and geographical settings

GRASS GIS Vector Processing: towards GRASS 7

The upcoming GRASS GIS 7 release improves not only raster processing and general design but the vector processing in the first place. GRASS GIS, as a topological GIS, recognizes that the topology plays the key role in the vector processing and analysis. Topology ensures that adjacent geographic components in a single vector map are related. In contrast to non-topological GIS, a border common to two areas exists only once and is shared between the two areas. Topological representation of vector data helps to produce and maintain vector maps with clean geometry as well as enables the user to perform certain analyses that can not be conducted with non-topological or spaghetti data. Non-topological vector data are automatically converted to a topological representation upon import. Further more, various cleaning tools exist to remove non-trivial topological errors. In the upcoming GRASS GIS 7 release the vector library was particularly improved to make it faster and more efficient with an improved internal vector file format. This new topological format reduces memory and disk space requirements, leading to a generally faster processing. Opening an existing vector requires less memory providing additionally support for large files. The new spatial index performs queries faster (compared to GRASS GIS 6 more than 10 times for large vectors). As a new option the user can select a file-based version of the spatial index for large vector data. All topological cleaning tools have been optimized with regard to processing speed, robustness, and system requirements. The topological engine comes with a new prototype for direct read/write support of Simple Features API/OGR. Additionally vector data can be directly exchanged with topological PostGIS 2 databases. Considering the wide spread usage of ESRI Shapefile, a non-topological format for vector data exchange, it is particularly advantageous that GRASS GIS 7 offers advanced cleaning tools. For power users and programmers, the new Python interface allows to directly access functions provided by the underlying C library; this combines the ease of writing Python scripts with the power of optimized C functionality in the library backend

Spatio-temporal data handling and visualization in GRASS GIS

Drowning in too many maps? Have some fun exploring fascinating geometries of changing landscapes in Space Time Cube and creating 2D and 3D animations from time series of geospatial data. Learn about the new capabilities for spatio-temporal data handling in GRASS GIS 7 (http://grass.osgeo.org/grass7/) and explore various techniques for dynamic visualizations. First, we will introduce you to GRASS GIS 7, including its spatio-temporal capabilities and you will learn how to manage and analyze geospatial data time series. Then, we will explore new tools for visualization of spatio-temporal data. You will create both 2D and 3D dynamic visualizations directly in GRASS GIS 7. Additionally, we will explain the Space Time Cube concept using various applications based on raster and vector data time series. You will learn to manage and visualize data in space time cubes (voxel models). No prior knowledge of GRASS GIS is necessary, we will cover the basics needed for the workshop. All relevant material including an overview of the tools and hands-on practical instructions along with the sample data sets will be available on-line. And, by the way, GRASS GIS is a free and open source geographic information system (GIS) used for geospatial data management, analysis, modeling, image processing, and visualization which runs on Linux, MS Windows, Mac OS X and other system

Analyzing rasters, vectors and time series using new Python interfaces in GRASS GIS 7

GRASS GIS 7 is a free and open source GIS software developed and used by many scientists (Neteler et al., 2012). While some users of GRASS GIS prefer its graphical user interface, significant part of the scientific community takes advantage of various scripting and programing interfaces offered by GRASS GIS to develop new models and algorithms. Here we will present different interfaces added to GRASS GIS 7 and available in Python, a popular programming language and environment in geosciences. These Python interfaces are designed to satisfy the needs of scientists and programmers under various circumstances. PyGRASS (Zambelli et al., 2013) is a new object-oriented interface to GRASS GIS modules and libraries. The GRASS GIS libraries are implemented in C to ensure maximum performance and the PyGRASS interface provides an intuitive, pythonic access to their functionality. GRASS GIS Python scripting library is another way of accessing GRASS GIS modules. It combines the simplicity of Bash and the efficiency of the Python syntax. When full access to all low-level and advanced functions and structures from GRASS GIS library is required, Python programmers can use an interface based on the Python ctypes package. Ctypes interface provides complete, direct access to all functionality as it would be available to C programmers. GRASS GIS provides specialized Python library for managing and analyzing spatio-temporal data (Gebbert and Pebesma, 2014). The temporal library introduces space time datasets representing time series of raster, 3D raster or vector maps and allows users to combine various spatio-temporal operations including queries, aggregation, sampling or the analysis of spatio-temporal topology. We will also discuss the advantages of implementing scientific algorithm as a GRASS GIS module and we will show how to write such module in Python. To facilitate the development of the module, GRASS GIS provides a Python library for testing (Petras and Gebbert, 2014) which helps researchers to ensure the robustness of the algorithm, correctness of the results in edge cases as well as the detection of changes in results due to new development. For all modules GRASS GIS automatically creates standardized command line and graphical user interfaces and documentation. Finally, we will show how GRASS GIS can be used together with powerful Python tools such as the NumPy package and the IPython Noteboo

PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS

Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves

Mathematical modelling for development of egocentric virtual environments

The aim of this work is to introduce a novel solution for Virtual Reality Cognitive Behaviour Therapy based on Virtual EgoCentric Holistic Environments. This is an alternative to the classical virtual environments (VEs) currently used and it is built on acquired user based information. The high-fidelity system is accompanied with several important attributes which will stimulate the human senses such as vision and hearing. An intensive study of these senses is leading us to answer an important question, what level of realism is required to provide effective immersive experience for the end users. We will demonstrate the potentials of the virtual egocentric holistic environments on three projects: virtual reality for the efficient treatment of infants with feeding difficulties, virtual reality therapeutic intervention for social anxiety, virtual reality for treatment of flying ants phobia. All these projects are multi-sensory and require visuals and audio to provide an appropriate level of realism

GRASS GIS: a peer-reviewed scientific platform and future research repository

Geographical Information System (GIS) is known for its capacity to spatially enhance the management of natural resources. While being often used as an analytical tool, it also represents a collaborative scientific platform to develop new algorithms. Thus, it is critical that GIS software as well as the algorithms are open and accessible to anybody [18]. We present how GRASS GIS, a free and open source GIS, is used by many scientists to implement and perform geoprocessing tasks. We will show how integrating scientific algorithms into GRASS GIS helps to preserve reproducibility of scientific results over time [15]. Moreover, subsequent improvements are tracked in the source code version control system and are immediately available to the public. GRASS GIS therefore acts as a repository of scientific peer-reviewed code, algorithm library, and knowledge hub for future generation of scientists. In the field of hydrology, with the various types of actual evapotranspiration (ET) models being developed in the last 20 years, it becomes necessary to inter-compare methods. Most of already published ETa models comparisons address few number of models, and small to medium areas [3, 6, 7, 22, 23]. With the large amount of remote sensing data covering the Earth, and the daily information available for the past ten years (i.e. Aqua/Terra-MODIS) for each pixel location, it becomes paramount to have a more complete comparison, in space and time. To address this new experimental requirement, a distributed computing framework was designed, and created [3, 4]. The design architecture was built from original satellite datasets to various levels of processing until reaching the requirement of various ETa models input dataset. Each input product is computed once and reused in all ETa models requiring such input. This permits standardization of inputs as much as possible to zero-in variations of models to the models internals/specificities. All of the ET models are available in the new GRASS GIS version 7 as imagery modules and replicability is complete for future research. A set of modules for multiscale analysis of landscape structure was added in 1992 by [1], who developed the r.le model similar to FRAGSTATS ([10]). The modules were gradually improved to become r.li in 2006. Further development continued, with a significant speed up [9] and new interactive user interface. The development of spatial interpolation module v.surf.rst started in 1988 [11] and continued by introduction of new interpolation methods and finally full integration into GRASS GIS version 4 [13]. Since then it was improved several times [8]. The module is an important part of GRASS GIS and is taught at geospatial modeling courses, for example at North Carolina State University [14]. GRASS GIS entails several modules that constitute the result of active research on natural hazard. The r.sim.water simulation model [12] for overland flow under rainfall excess conditions was integrated into the Emergency Routing Decision Planning system as a WPS [17]. It was also utilized by [16] and is now part of Tangible Landscape, a tangible GIS system, which also incorporated the r.damflood, a dam break inundation simulation [2]. The wildfire simulation toolset, originally developed by [24], implementing Rothermel’s model [21], available through the GRASS GIS modules r.ros and r.spread, is object of active research. It has been extensively tested and recently adapted to European fuel types ([5, 19, 20]

TANGIBLE LANDSCAPE: COGNITIVELY GRASPING THE FLOW OF WATER

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow