Spatio Temporal Analysis

After elimination of the Aedes aegypti vector in South America in the 1960s, dengue outbreaks started to reoccur during the 1990s; strongly in Argentina since 1998. In 2016, Córdoba City had the largest dengue outbreak in its history. In this article we report this outbreak including spatio-temporal analysis of cases and vectors in the city. A total of 653 dengue cases were recorded by the laboratory-based dengue surveillance system and georeferenced by their residential addresses. Case maps were generated from the epidemiological week 1 (beginning of January) to week 19 (mid-May). Dengue outbreak temporal evolution was analysed globally and three specific, high-incidence zones were detected using Knox analysis to characterising its spatio-temporal attributes. Field and remotely sensed data were collected and analysed in real time and a vector presence map based on the MaxEnt approach was generated to define hotspots, towards which the pesticide- based strategy was then targeted. Th...

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and valid...

Malaria control programmes across Africa and beyond are facing increasing insecticide resistance in the major anopheline vectors. In order to preserve or prolong the effectiveness of the main malaria vector interventions, up-to-date and easily accessible insecticide resistance data that are interpretable at operationally-relevant scales are critical. Herein we introduce and demonstrate the usefulness of an online mapping tool, IR Mapper. A systematic search of published, peer-reviewed literature was performed and Anopheles insecticide susceptibility and resistance mechanisms data were extracted and added to a database after a two-level verification process. IR Mapper (www.irmapper.com) was developed using the ArcGIS for JavaScript Application Programming Interface and ArcGIS Online platform for exploration and projection of these data. Literature searches yielded a total of 4,084 susceptibility data points for 1,505 populations, and 2,097 resistance mechanisms data points for 1,000 ...

Causa e effetto invertiti, entropia e sintropia, ordine e disordine, origine e scopo. Agli occhi della scienza il Tempo rimane ancora un enigma. Dall’America alla Russia passando per l’Europa, i misteri più affascinanti della quarta dimensione.

During recent decades, Burundi was threatened by recurrent dry events resulting in crop failure and more socioeconomic losses. To assess the drought occurrence, duration and intensity, this study used the Standardized Precipitation Index (SPI) at 3-, 6-and 12-month time scales for the period 1981-2017. The Mann Kendall and Modified Mann Kendall trend tests and Sen's slope statistic test were used to analyse the spatiotemporal drought trend. The overall analysis of SPI-3, SPI-6 and SPI-12 results revealed that the northern part of Burundi was the most threatened by dry events, where more than 80% of the extremely and severely dry events occurred within the period 1993-2000. The drought magnitude highly varied during the short rains season (SOND) specifically during the 1990s decade. The cumulative frequency of extremely dry events was very high in the North with 5.2%, 6.1% and 7.4% at 3-, 6-and 12-month time scales respectively. Likewise, the northern part experienced short, medi...

The rise of the Greek film industry during the 1950s and 1960s was enjoyed not just in Greece but internationally throughout the Greek diaspora. This was especially apparent in Australia, where waves of post-war migration provided a ready audience for imported Greek cinema. This paper will examine the relationship between domestic and diasporic film industries focussing on the distribution arrangements between Greek suppliers and Australian distributors.

This thesis demonstrates the use of deep learning for automating hourly price forecasts in continuous intraday electricity markets, using various types of neural networks on comprehensive sequential market data and cutting-edge image processing networks on spatio-temporal weather forecast maps. Deep learning is a subfield of artificial intelligence that excels on problems such as these with multifarious input data and manifold interacting factors. It has seen tremendous success on a range of problems across industries, and while it is important to have realistic expectations, there is little reason to believe that intraday electricity markets are different. Focusing on Nord Pool’s intraday market Elbas, we predict Nordic buyers’ volume-weighted average price over the last six hours of trading prior to each delivery hour. Aggregating this window gives buyers flexibility from many trades and sufficient time in which to act on the predictions, and solves issues with data sparsity while keeping sufficient resolution for predictions to be informative.

We develop various neural networks via extensive experimentation, with inspiration from other research and problem domains. To make the findings relevant in practice, we impose constraints on the input data based on what would be available to Elbas market participants six trading hours ahead of delivery. The neural networks are benchmarked against a set of simple domain- based heuristics and traditional methods from econometrics and machine learning. We conclude with a holistic evaluation of the efficacy of deep learning on our problem, whether it is economically justifiable in light of its value-add, what the salient hurdles are to implementing it in practice, and what the implications are for broader applications of AI in intraday markets.

The deep learning models are relatively accurate and reliable under normal market conditions. The average price across all delivery hours in the held-out data is 30.95 EUR/MWh, where our best network is on average off by 2.72 EUR/MWh. It beats the best simple heuristic by 21–25%, and the best benchmark model by 12–16%. The network also anticipates major fluctuations in prices relatively consistently, and generally outperforms all alternative methods when prices are especially volatile or trading activity particularly high. In contrast to the benchmarks, there are also ample avenues for improving the network further. Beyond being promising in its own right, we also argue that the network demonstrates the wider potential of deep learning in a range of applications in intraday markets, and that these are worthy of serious consideration — though one should be aware of the practical hurdles to implementing them operationally.

We focus on the case of “symmetry” in the visual and cinematographic structure of the moving image, and present a multi-level model of interpreting symmetric patterns therefrom. Whereas our computational model is general, we particularly focus (in this presentation) on the works of Wes Anderson as one case-study for its compelling, vivid, and holistic use of symmetry in a range of aspects such as character placement, editing, and even on-screen character interactions at the level of the screenplay.
Our model provides the foundation for integrating scene analysis with the analysis of its visuo-spatial perception based on eyetracking data. This is achieved by the integration of: computational semantic interpretation of the scene —involving scene objects (people, objects in the scene), cinematographic aids (camera movement, shot types, cuts and scene structure)— and perceptual artefacts (fixations, saccades, scan-path, areas of attention).

Approximately 74 thousand years ago (ka), the Toba caldera erupted in Sumatra. Since the magnitude of this eruption was first established, its effects on climate, environment and humans have been debated. Here we describe the discovery of microscopic glass shards characteristic of the Youngest Toba Tuff-ashfall from the Toba eruption-in two archaeological sites on the south coast of South Africa, a region in which there is evidence for early human behavioural complexity. An independently derived dating model supports a date of approximately 74 ka for the sediments containing the Youngest Toba Tuff glass shards. By defining the input of shards at both sites, which are located nine kilometres apart, we are able to establish a close temporal correlation between them. Our high-resolution excavation and sampling technique enable exact comparisons between the input of Youngest Toba Tuff glass shards and the evidence for human occupation. Humans in this region thrived through the Toba even...

The research objectives were to examine the concentration of crime and its temporal patterns in Niš and compare the related research results in spatial criminology. Longitudinal property and violent crime data were geocoded and assigned to three types of grid cells. Descriptive statistics were used to examine crime concentration, and spatial stability of crime was tested using a spatial point pattern test. The findings suggest an intense concentration of all crime types within all areal units and years of study. The higher rates of concentration were found in the analysis of less common robbery and violent crime. Findings of spatial pattern analysis suggest moderate to a high degree of similarity in spatial patterns among all crime types. However, sensitivity analysis indicates significant changes in the spatial distribution of both property and violent crime. Therefore, an alternative method is applied. Theoretical implications are discussed, highlighting routine activities and crime pattern theory. The study aims to instigate more spatial analysis of crime and evidence based criminal policies within the region.

Seasonal crime patterns have been a topic of criminological research for more than a century with results often conflicting. This descriptive qualitative study examines the nature of property crimes in Dhaka city and whether crime patterns fluctuate periodically throughout the seasons in between 2013 to 2016 using specific property crime data of Dhaka metropolitan area by following content analysis. This study found winter season is more likely to peak in property crime (26.54%). Among the property crimes dacoity peaks at monsoon (31.13% of total dacoity), robbery peaks at summer (26.62%) and vehicle theft touches the top (27.13%) at winter season. This result data has resemblance with many of researches before those worked with spatial-temporal relationships.

West Bengal is a state with a large amount of geographical and physical variation and in relation to this the socioeconomic and political variation also prominent here. So, the crime pattern also varies from one area to another area with the changing geographical, physical, socioeconomic and political conditions. It is noticeable that the rapid growth of urbanization in different districts, mainly the expansion of Kolkata, the high growth of population, the international border areas, the forested areas, the industrial and mine areas are most vulnerable areas in terms of occurrence of criminal activities. North and South 24 Parganas and Kolkata always fall under very high crime intensity zone and Purulia, Bnkura, Birbhum, Purba Midnapore, Coochbehar, Malda, Uttar Dinajpur and South Dinajpur always lie under low to moderate crime intensity zone. On the contrary for the rest of the districts, viz, Paschim Midnapore, Burdwan, Howrah, Hooghly, Darjeeling, Jalpaiguri and Alipurduar, Murshidabad and Nadia, the crime intensity zones vary from low to very high over changing of time period. Maximum variation is prominent for Jalpaiguri-Alipurduar districts. This pattern of variation may be for the influence of different factors in occurrence of crime like socio-political factors, socio-psychological factors, socioeconomic factors and finally the geographical and demographic factor. The Criminal Procedure Code of India classifies all types of crime in two categories; firstly, Cognizable Crime including the crime falling either under the `Indian Penal Code (IPC) or under the `Special and Local Laws (SLL)' and secondly, Non Cognizable Crime. In The IPC crimes, 23 type of crimes fall under major six crime categories like crime against body, crime against property, crime against public order, crime against women, economic crime and other IPC Crimes. Most dominating crime types for West Bengal are Attempt to commit murder, Hurt or grievous hurt, Preparation and assembly to commit dacoity, Counterfeiting, Culpable homicide not amounting to murder and Cruelty by husband or his relatives. Highest crime intensity zone are around highly populated areas with increasing rate of urbanization, border areas, and forested areas. In Urban areas ma jor crimes are Murder, theft, Burglary, Counterfeiting, Crime against Women, Cheating and Criminal breach of trust. In case of Forested areas, Theft, Dacoity, Poaching, Corruption are most reported crimes. In areas with National and International Border, major type of crimes that are experiencing are like Smuggling, Trafficking, Murder, Robbery, Dacoity, Crime against women, kidnapping and abduction. Rural areas consist crimes like Crime against women, theft, and arson type of crimes.

Crime data analysts can assist law enforcement officers in accelerating the crime prediction process with the rising advent of computerized systems. This survey paper, therefore, presents a description of the tools and strategies used in the study and forecast of crime data. Crime forecasts are a way to exploit and reduce future crime by anticipating when future crime will happen. Crime prediction relates historical data and predicts the next crime, based on place, time, day, season, and year after analyzing data.This paper provides a fair review of numerous discoveries and predictions of potential crime

Tutoriel pour formaliser, modéliser, représenter et diffuser des données spatio-temporelles

- QGIS et l'extension Time manager
- CartoDB et la fonctionnalité Torque

Données pour les exercices :
https://dl.dropboxusercontent.com/u/39122527/DATA%20seance%202%20%28B.%20Mericskay%29.zip

Contemporary geographical thought is constrained by a political economic imagination rooted in binarism, which is exemplified in debates surrounding neoliberalism. Neoliberal proponents call for decentralization and increased capital flows, while Marxists respond by pairing centralization with capitalism’s abrogation. The latter view considers hierarchy necessary, a position that promotes authority and regards horizontal politics as propitious to neoliberalism. Anarchism’s coupling of decentralization with anti-capitalism is dismissed because Marxism cannot accommodate the processuality of prefigurative politics. Marxism demands a revolution with a masterplan, considering horizontality a future objective. Such a temporality ignores the insurrectionary possibilities of the present and implies a politics of waiting. The spatial implications of centralized hierarchy are also questionable, employing a vertical ontology, wherein horizontal organization is deemed inappropriate when ‘jumping scales’. Yet scale represents both a theoretical dis-traction from grounded everyday particularities and a ‘master-signifier’ by providing a point de capiton, or anchoring point, that rests on the exclusion of unconsciousness–the knowledge that is not known¬. Thus the point de capiton is the (Archimedean) point at which an essentialist illusion of fixed meaning is created, as scale is unconscious of geography’s ‘hidden enfolded immensities’. The discourse of scale accordingly dismisses the openness of rhizomic politics by predetermining the political as an arborescent register. Yet the inevitable terra incognita that scalar hierarchies produce becomes a powerful resource for the oppressed, which is why anarchist direct action often proceeds outside of authority’s view. A flat ontology has significant resonance with anarchism, imparting that politics should operate horizontally rather than vertically. This ontological shift suggests that we need not wait for the emergence of a ‘greater’ class-consciousness, as one can immediately disengage capitalism by reorienting economic landscapes in alternative ways. Consequently, a human geography without hierarchy gains significant traction when we reject scale and embrace an anarchist flat ontology.