Concept: Geographic information system
How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends.
In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.
The abundant dinosaurian tracksites of the Lower Cretaceous (Valanginian-Barremian) Broome Sandstone of the Dampier Peninsula, Western Australia, form an important part of the West Kimberley National Heritage Place. Previous attempts to document these tracksites using traditional mapping techniques (e.g., surface overlays, transects and gridlines combined with conventional photography) have been hindered by the non-trivial challenges associated with working in this area, including, but not limited to: (1) the remoteness of many of the tracksites; (2) the occurrence of the majority of the tracksites in the intertidal zone; (3) the size and complexity of many of the tracksites, with some extending over several square kilometres. Using the historically significant and well-known dinosaurian tracksites at Minyirr (Gantheaume Point), we show how these issues can be overcome through the use of an integrated array of remote sensing tools. A combination of high-resolution aerial photography with both manned and unmanned aircraft, airborne and handheld high-resolution lidar imaging and handheld photography enabled the collection of large amounts of digital data from which 3D models of the tracksites at varying resolutions were constructed. The acquired data encompasses a very broad scale, from the sub-millimetre level that details individual tracks, to the multiple-kilometre level, which encompasses discontinuous tracksite exposures and large swathes of coastline. The former are useful for detailed ichnological work, while the latter are being employed to better understand the stratigraphic and temporal relationship between tracksites in a broader geological and palaeoecological context. These approaches and the data they can generate now provide a means through which digital conservation and temporal monitoring of the Dampier Peninsula’s dinosaurian tracksites can occur. As plans for the on-going management of the tracks in this area progress, analysis of the 3D data and 3D visualization will also likely provide an important means through which the broader public can experience these spectacular National Heritage listed landscapes.
BACKGROUND: A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost effective manner. METHODS: The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. RESULTS: A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. CONCLUSIONS: The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This method provides an important technique that can be applied to other developing countries where a randomized study design is needed but infrastructure is lacking to implement more traditional participant selection methods.
To estimate the areas of greatest density of road traffic accidents with fatalities at 24hours per km(2)/year in Spain from 2008 to 2011, using a geographic information system.
- Brazilian journal of biology = Revista brasleira de biologia
- Published about 2 years ago
The multitemporal behavior of soil loss by surface water erosion in the hydrographic basin of the river Mourão in the center-western region of the Paraná state, Brazil, is analyzed. Forecast was based on the application of the Universal Soil Loss Equation (USLE) with the data integration and estimates within an Geography Information System (GIS) environment. Results had shown high mean annual rain erosivity (10,000 MJ.mm.ha-1.h-1.year-1), with great concentration in January and December. As a rule, soils have average erodibilities, exception of Dystroferric Red Latisol (low class) and Dystrophic Red Argisol (high class). Although the topographic factor was high (>20), rates lower than 1 were predominant. Main land uses comprise temporal crops and pasture throughout the years. The watershed showed a natural potential for low surface erosion. When related to usage types, yearly soil loss was also low (<50 ton.ha-1.year-1), with more critical scores that reach rates higher than 150 ton.ha-1.year-1. Soil loss over the years did not provide great distinctions in distribution standards, although it becames rather intensified in some sectors, especially in the center-eastern and southwestern sections of the watershed.
- Proceedings of the National Academy of Sciences of the United States of America
- Published 11 months ago
Previous studies have shown that it is possible to detect macroscopic patterns of cultural change over periods of centuries by analyzing large textual time series, specifically digitized books. This method promises to empower scholars with a quantitative and data-driven tool to study culture and society, but its power has been limited by the use of data from books and simple analytics based essentially on word counts. This study addresses these problems by assembling a vast corpus of regional newspapers from the United Kingdom, incorporating very fine-grained geographical and temporal information that is not available for books. The corpus spans 150 years and is formed by millions of articles, representing 14% of all British regional outlets of the period. Simple content analysis of this corpus allowed us to detect specific events, like wars, epidemics, coronations, or conclaves, with high accuracy, whereas the use of more refined techniques from artificial intelligence enabled us to move beyond counting words by detecting references to named entities. These techniques allowed us to observe both a systematic underrepresentation and a steady increase of women in the news during the 20th century and the change of geographic focus for various concepts. We also estimate the dates when electricity overtook steam and trains overtook horses as a means of transportation, both around the year 1900, along with observing other cultural transitions. We believe that these data-driven approaches can complement the traditional method of close reading in detecting trends of continuity and change in historical corpora.
Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.
BACKGROUND: Despite evidence to suggest significant spatial variation in out-of-hospital cardiac arrest (OHCA) and bystander cardiopulmonary resuscitation (BCPR) rates, geographic information systems (GIS) and spatial analysis have not been widely used to understand the reasons behind this variation. This study employs spatial statistics to identify the location and extent of clusters of bystander CPR in Houston and Travis County, Texas. METHODS: Data were extracted from the Cardiac Arrest Registry to Enhance Survival for two U.S. sites - Austin-Travis County EMS and the Houston Fire Department - between October 1, 2006 and December 31, 2009. Hierarchical logistic regression models were used to assess the relationship between income and racial/ethnic composition of a neighborhood and BCPR for OHCA and to adjust expected counts of BCPR for spatial cluster analysis. The spatial scan statistic was used to find the geographic extent of clusters of high and low BCPR. RESULTS: Results indicate spatial clusters of lower than expected BCPR rates in Houston. Compared to BCPR rates in the rest of the community, there was a circular area of 4.2km radius where BCPR rates were lower than expected (RR=0.62; p<0.0001 and RR=0.55; p=0.037) which persist when adjusted for individual-level patient characteristics (RR=0.34; p=0.027) and neighborhood-level race (RR=0.34, p=0.034) and household income (RR=0.34, p=0.046). We also find a spatial cluster of higher than expected BCPR in Austin. Compared to the rest of the community, there was a 23.8km radius area where BCPR rates were higher than expected (RR=1.75, p=0.07) which disappears after controlling for individual-level characteristics. CONCLUSIONS: A geographically targeted CPR training strategy which is tailored to individual and neighborhood population characteristics may be effective in reducing existing disparities in the provision of bystander CPR for out-of-hospital cardiac arrest.
Injuries are a major public health problem around the world. Previous research has suggested that providing prompt access to specialized trauma center care may greatly improve the health outcomes of trauma patients. In this paper, a geographic information system (GIS) method is used to examine potential spatial access to trauma centers by individuals who were either hospitalized or died as a result of a major trauma. Overall, it was determined that 68.5% of individuals who suffered from a major trauma lived within one hour travel time of a Level I or II trauma center. In addition, major traumas resulting in death were found to have poorer potential spatial access to trauma center care than those that were admitted to hospital.