Uncovering spatiotemporal micromobility patterns through the lens of space–time cubes and GIS tools

Journal of Geographical Systems - In the past ten years, cities have experienced a burst of micromobility services as they offer a flexible transport option that allows users to cover short trips...     

Multi‐Scale Qualitative Location: A Topology‐Based Model

Qualitative locations describe spatial objects by relating the spatial objects to a frame of reference (e.g. a regional partition in this study) with qualitative relations. Existing models only formalize spatial objects, frames of reference, and their relations at one scale, thus limiting their applicability in representing location changes of spatial objects across scales. A topology‐based, multi‐scale qualitative location model is proposed to represent the associations of multiple representations of the same objects with respect to the frames of reference at different levels. Multi‐scale regional partitions are first presented to be the frames of reference at multiple levels of scale. Multi‐scale locations are then formalized to relate multiple representations of the same objects to the multiple frames of reference by topological relations. Since spatial objects, frames of reference, and topological relations in qualitative locations are scale dependent, scale transformation approaches are presented to derive possible coarse locations from detailed locations by incorporating polygon merging, polygon‐to‐line and polygon‐to‐point operators.     

A Combined Biophysical and Economic GIS Framework to Assess Sugarcane Cropping Potential in Brazil

Recently, the increasing demand for biofuels triggered a new phase for the sugar‐alcohol sector. In Brazil, as well as in other tropical countries, this process raised worries regarding the possible direct and indirect effects of the crop's expansion on the conversion of native vegetation coverings. Therefore, the modeling of spatial‐economic surfaces, representing the potential rent variation in its spatial component, for economic activities, may be a useful tool in the decision‐making process. Hence, here we propose and present the results of a combined framework composed of two modules using the modeling platform Dinamica EGO. The first module simulates sugarcane's growth, calculating the daily response of the crop to environmental conditions during the stages of the plant's development. The second module estimates rents for sugarcane cultivation in Brazil, identifying areas where this activity would bring higher economic return, looking at simulated productivity, production costs and selling prices in a way that is spatially explicit for Brazil. Two different scenarios for production costs were tested, and results ranged from negative values to maxima of R$/ha 929 and R$/ha 1176 for standard and efficient costs of production, respectively. The model successfully indicated non‐profitable and profitable areas, and regions where high expected economic return overlaps endangered ecosystems.     

CPLat: first operational experimental processing center for SIRGAS in Argentina

The SIRGAS permanent GPS network which is in fact the IGS network densification for the American continent, consists today of more than 200 stations covering the continent and islands. It is currently processed by the IGS RNAAC SIR centre at Deutsches Geodätisches Forschungsinstitut producing weekly free solutions relying on IGS final orbits and EOP that contribute to the ITRF through IGS. By August 2006, the SIRGAS Working Group I had accepted five proposals for experimental processing centers within the region that would collaborate with IGS RNAAC SIR. One of them, Centro de Procesamiento La Plata (CPLat) in Argentina, began processing 60 stations on October 2006. By January 2007 CPLat reached operational capability, delivering weekly free solution SINEX files, with an internal consistency of 1.5 mm average for the horizontal components, and 3 mm in the vertical. Comparisons with IGS global and IGS RNAAC SIR weekly solutions were taken as external consistency indications, showing average RMS residuals of 1.8, 2.4 and 5 mm for the north, east, and vertical component, respectively. Analysis and comparison of adjusted solution time series from CPLat and other processing centers has proved to be highly valuable for solution QC, namely detection and identification of station anomalous behavior or modelling problems. These procedures will ensure the maintenance of the performance specifications for CPLat solutions. Action is being taken in order to guarantee the continuity of this effort beyond the experimental phase.     

Possibilities of GPS Kinematic Measurements for Geological Survey in East Slovakia

This paper deals with transformation procedures for observed GPS data from the world geodetic system WGS-84 into the national geodetic grid datum S-UTCN(system of united trigonometric cadaster network) and Baa(the Baltic Sea after adjustment).Transformation from WGS-84 into SUTCN is performed most frequently by means of the 7-element Helmert transformation with three identical points.Geodetic network was adjusted by two ways.     

Evaluation of pre-CHAMP gravity models `GRIM5-S1' and `GRIM5-C1' with satellite crossover altimetry

 The new GFZ/GRGS gravity field models GRIM5-S1 and GRIM5-C1, currently used as initial models for the CHAMP mission, have been compared with other recent models (JGM 3, EGM 96) for radial orbit accuracy (by means of latitude lumped coefficients) in computations on altimetry satellite orbits. The bases for accuracy judgements are multi-year averages of crossover sea height differences from Geosat and ERS 1/2 missions. This radially sensitive data is fully independent of the data used to develop these gravity models. There is good agreement between the observed differences in all of the world's oceans and projections of the same errors from the scaled covariance matrix of their harmonic geopotential coefficients. It was found that the tentative scale factor of five for the formal standard deviations of the harmonic coefficients of the new GRIM fields is justified, i.e. the accuracy estimates, provided together with the GRIM geopotential coefficients, are realistic. Received: 20 February 2001 / Accepted: 24 October 2001     

Multifrequency algorithms for precise point positioning: MAP3

We present the new MAP3 algorithms to perform static precise point positioning (PPP) from multifrequency and multisystem GNSS observations. MAP3 represents a two-step strategy in which the least squares theory is applied twice to estimate smoothed pseudo-distances, initial phase ambiguities, and slant ionospheric delay first, and the absolute receiver position and its clock offset in a second adjustment. Unlike the classic PPP technique, in our new approach, the ionospheric-free linear combination is not used. The combination of signals from different satellite systems is accomplished by taking into account the receiver inter-system bias. MAP3 has been implemented in MATLAB and integrated within a complete PPP software developed on site and named PCube. We test the MAP3 performance numerically and contrast it with other external PPP programs. In general, MAP3 positioning accuracy with low-noise GPS dual-frequency observations is about 2.5 cm in 2-h observation periods, 1 cm in 10 h, and 7 mm after 1 day. This means an improvement in the accuracy in short observation periods of at least 7 mm with respect to the other PPP programs. The MAP3 convergence time is also analyzed and some results obtained from real triple-frequency GPS and GIOVE observations are presented.     

Deforestation and fragmentation of seasonal tropical forests in the southern Yucatán,Mexico (1990–2006)

The southern Yucatán (SY) has been recognized as a hotspot of biodiversity with great risk of deforestation. Land change analysis, based on classified Landsat TM and ETM?+?satellite imagery (1990, 2000 and 2006), was used to estimate the annual deforestation rates of 141 land management units of the SY, and spatial patterns of forest fragmentation around and within the Calakmul Biosphere Reserve (CBR), which comprises approximately one-third of the region. Results indicate a decrease in annual deforestation rates over 1990–2006, from 0.15% year^?1 to 0.06% year^?1, but with significant sub-regional variations in the quantity and rate of forest loss. Despite a decline in deforestation during this period, there was considerable fragmentation both inside and outside the CBR. While population pressures and the expansion of pasture have caused deforestation across the region, agricultural intensification, diversified income strategies and reserve conservation may have contributed to reduced forest loss during the study period.     

Context inference and prediction modeling in ubiquitous health GIS

The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, weather data and geographical information for 270 different situations. Our results showed that 90% of the system's predictions were correct. The proposed model also improved the estimation accuracy by 15% in comparison to conventional methods.