Enabling Spatio-Temporal Search in Open Data

Objectives: Research evidence on schools as a factor in the distribution of neighborhood violence has produced varying and at-times directly contradictory results. Drawing conclusions from existing research is also complicated by data limitations and methodological differences. The present study sought to further research in this area using a novel open-data source.Methods: Police-recorded assault and personal robbery data from nine large US cities were used to test four hypotheses (derived from the routine activities approach) on spatio-temporal patterns of violence around schools. Multi-level Markov Chain Monte Carlo models were used to reflect the clustered structure of the data.Results: The presence of a public secondary (middle or high) school in a census block group was associated with higher daytime assault and robbery counts on weekdays when schools were in session but not on non-school weekdays, and the effect was larger for larger schools. No such relationships were found for elementary schools. However, there were variations between cities, in that there was no effect in one city and the effect sizes in other cities varied substantially.Conclusions: The results were consistent with the routine activites approach, suggesting a role for middle and high schools in the distribution of neighbourhood violence. The differences between cities suggest that studying multiple cities is important in the investigation of crime and place, and that open data may provide a mechanism for overcoming the data-access difficulties that have previously limited multi-city studies of spatio-temporal variations in crime.

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Environmental Spatio-temporal Ontology for the Linked Open Data Cloud

2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications ◽

10.1109/trustcom.2013.279 ◽

2013 ◽

Cited By ~ 3

Author(s):

Ahsan Morshed ◽

Jagannath Aryal ◽

Ritaban Dutta

Keyword(s):

Open Data ◽

Linked Open Data ◽

Temporal Ontology ◽

Spatio Temporal

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Topical and Spatio-Temporal Search over Distributed Online Databases

Advances in Geospatial Technologies - Qualitative Spatio-Temporal Representation and Reasoning ◽

10.4018/978-1-61692-868-1.ch013 ◽

2012 ◽

pp. 386-403

Author(s):

Nikos Zotos ◽

Sofia Stamou

Keyword(s):

Temporal Orientation ◽

Similarity Metrics ◽

Spatial Similarity ◽

Online Databases ◽

Geographic Coverage ◽

Contextual Elements ◽

Temporal Expressions ◽

Spatio Temporal ◽

Database Content ◽

Temporal Search

In this chapter, the authors propose a novel framework for the support of multi-faceted searches over distributed Web-accessible databases. Towards this goal, the authors introduce a method for analyzing and processing a sample of the database contents in order to deduce the topical, the geographic, and the temporal orientation of the entire database contents. To extract the database topics, the authors apply techniques leveraged from the NLP community. To identify the database geographic footprints, the authors first rely on geographic ontologies in order to extract toponyms from the database content samples and then employ geo-spatial similarity metrics to estimate the geographic coverage of the identified toponyms. Finally, to determine the time aspects associated with the database entities, the authors extract temporal expressions from the entities’ contextual elements and utilize a time ontology against which the temporal similarity between the identified entities is estimated.

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Uncovering Spatio-temporal Air Pollution Exposure Patterns During Commutes to Create an Open-Data Endpoint for Routing Purposes

10.1007/978-3-030-74590-5_6 ◽

2021 ◽

pp. 115-151

Author(s):

Bram Vandeninden ◽

Charlotte Vanpoucke ◽

Olav Peeters ◽

Jelle Hofman ◽

Christophe Stroobants ◽

...

Keyword(s):

Air Pollution ◽

Open Data ◽

Air Pollution Exposure ◽

Exposure Patterns ◽

Spatio Temporal ◽

Pollution Exposure

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Spatio-temporal patterns and extremity assessment of heavy rainfall events in Germany, derived from a radar-based catalogue (CatRaRE, 2001-2020)

10.5194/ems2021-461 ◽

2021 ◽

Author(s):

Ewelina Walawender ◽

Katharina Lengfeld ◽

Tanja Winterrath ◽

Elmar Weigl ◽

Andreas Becker

Keyword(s):

Heavy Rainfall ◽

Open Data ◽

Heavy Precipitation ◽

Radar Data ◽

Rainfall Events ◽

Antecedent Precipitation ◽

Hourly Data ◽

Spatio Temporal ◽

Heavy Rainfall Events ◽

Precipitation Events

One of the predicted effects of climate change in Central Europe is a growing number and increasing extremity of heavy rainfalls. Thus, it is of a great importance not only to develop best possible nowcasting methods and long-term forecasting models, but also to look closer at the structure and detailed characteristics of extreme events that have already taken place.With this objective, the German Weather Service (DWD) has developed a Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 20 years of climatological radar data for the area of Germany.Using hourly data of about 1 km spatial resolution, an object-oriented analysis is performed to classify spatially and timely independent rainfall events exceeding the official warning level for heavy precipitation. Events with duration between 1 and 72 hours are investigated and statistically analysed. Apart from various extremity attributes, like return period, heavy precipitation, and weather extremity indices, the catalogue is enriched with additional variables (e.g. weather type, antecedent precipitation index, population density, land cover, imperviousness degree, Topographic Position Index), providing the meteorological background and helping to estimate the possible impact, each event could provoke.The Catalogue is freely available via DWD&#8217;s Open Data Portal in both a tabular and spatial (GIS) format. In addition, a user friendly online Dashboard was developed to visualize the data and communicate our results to a broader audience.&#160;We will present the CatRaRE Catalogue and results of a comprehensive analysis of all classified heavy precipitation events that occurred in Germany between 2001 and 2020. Different time scales from diurnal to multi-annual, as well as identified spatial patterns in connection with event attributes will be illustrated. Most common weather types, favouring occurrence of detected events will be outlined. Finally, we will demonstrate selected application possibilities by combining the catalogue with other datasets (e.g. fire brigade operations).

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Probabilistic models of water resources management on urbanized areas

Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics ◽

10.17721/1812-5409.2020/4.3 ◽

2020 ◽

pp. 22-27

Author(s):

N. M. Kizilova ◽

N. L. Rychak

Keyword(s):

Climate Change ◽

Water Resources ◽

Probabilistic Models ◽

Global Climate ◽

Measurement Data ◽

Open Data ◽

Probability Models ◽

Urban Territories ◽

Spatio Temporal ◽

Mathematical Sciences

Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.

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