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Movements, leaders, victories and the continuing fight for equality.
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Movements, leaders, victories and the continuing fight for equality.
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In 1968 Shirley Chisholm became the first Black woman elected to the United States Congress. In 1972 she launched a campaign for the Democratic presidential nomination, breaking another political barrier.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
| Organisation | International Council for the Exploration of the Sea |
|---|---|
| No. issued | 11,187 (standard resolution rectangles), 100,683 (sub-rectangles) |
| No. of digits | 4 (standard resolution rectangle), 5 (sub-rectangle) |
| Example | 37F3 (standard resolution rectangle), 37F31 (sub-rectangle) |
| Website | www |
ICES Statistical Rectangles (aka "ICES Rectangles") is a gridded, latitude-longitude based area notation system covering the north-east Atlantic region developed by the International Council for the Exploration of the Sea (ICES) in the 1970s, for simplified analysis and visualization of spatial data of relevance to that organization's interests.[1] The individual rectangles that make up the system each measure 1 degree of longitude by 0.5 degrees of latitude and are intended to be roughly square in real world use in the ICES region of interest, approximately 30 nautical miles by 30 nautical miles (55×55 km) at 60°N, although the actual width varies with latitude, gradually becoming wider than they are high south of 60°N, and narrower further north. The grid covers the region from 36°N to 85°30'N and from 44°W to 69°E (quoted as 68°30') using a set of alphanumeric identifiers, with rows of latitude (identifiers 01 through 99, from south to north) cited first, then column of longitude (identifiers A0-A3, then B0-B9, C0-C9 etc., from west to east). The last used column identifier is M8; column identifiers A4-A9, and prefix "I" (uppercase "i") i.e. columns "I"0-"I"9 are not used. The resulting grid is 113 columns by 99 rows, comprising 11,187 labelled 1×0.5 degree cells. An example cell designation is 37F3, which designates the 1×0.5 degree rectangle of which the south-west corner is 54°00'N, 03°00'E.[2][3] The grid covers both land and sea areas across its designated region, but as per the interests of its originating body, is typically employed for use with marine data such as analysis of marine resources, fishing activities, seabed habitat, etc., refer example references below. The full extent of the grid is visible in published figures such as Figs. 5-8 in Williamson et al., 2017.[4]
To accommodate the visualization and/or analysis of finer scale data than is supported by the "standard" (1×0.5 degree) rectangles, an optional fifth character (as digits 1 through 9) can be used which will place a location within a finer 9-cell (3 by 3) sub-grid, for which the individual sub-rectangles then measure 20' of longitude by 10' of latitude (approximately 10×10 nautical miles). Sub-rectangle designations go north to south, then west to east, with 1 at the north west corner and 9 at the south east.[2]
ICES rectangles have been used for reporting and analysis in numerous marine publications since the 1970s; for recent examples see.[5][6][7] A vector polygon representation of the ICES Statistical Rectangles is available here.
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
ICES Statistical Rectangles (aka "ICES Rectangles") is a gridded, latitude-longitude based area notation system covering the north-east Atlantic region developed by the International Council for the Exploration of the Sea (ICES) in the 1970s, for simplified analysis and visualization of spatial data of relevance to that organization's interests. The individual rectangles that make up the system each measure 1 degree of longitude by 0.5 degrees of latitude and are intended to be roughly square in real world use in the ICES region of interest, approximately 30 nautical miles by 30 nautical miles (55×55 km) at 60°N, although the actual width varies with latitude, gradually becoming wider than they are high south of 60°N, and narrower further north. The grid covers the region from 36°N to 85°30'N and from 44°W to 69°E (quoted as 68°30') using a set of alphanumeric identifiers, with rows of latitude (identifiers 01 through 99, from south to north) cited first, then column of longitude (identifiers A0-A3, then B0-B9, C0-C9 etc., from west to east). The last used column identifier is M8; column identifiers A4-A9, and prefix "I" (uppercase "i") i.e. columns "I"0-"I"9 are not used. The resulting grid is 113 columns by 99 rows, comprising 11,187 labelled 1×0.5 degree cells. An example cell designation is 37F3, which designates the 1×0.5 degree rectangle of which the south-west corner is 54°00'N, 03°00'E. The grid covers both land and sea areas across its designated region, but as per the interests of its originating body, is typically employed for use with marine data such as analysis of marine resources, fishing activities, seabed habitat, etc., refer example references below. The full extent of the grid is visible in published figures such as Figs. 5-8 in Williamson et al., 2017. To accommodate the visualization and/or analysis of finer scale data than is supported by the "standard" (1×0.5 degree) rectangles, an optional fifth character (as digits 1 through 9) can be used which will place a location within a finer 9-cell (3 by 3) sub-grid, for which the individual sub-rectangles then measure 20' of longitude by 10' of latitude (approximately 10×10 nautical miles). Sub-rectangle designations go north to south, then west to east, with 1 at the north west corner and 9 at the south east. ICES rectangles have been used for reporting and analysis in numerous marine publications since the 1970s; for recent examples see. A vector polygon representation of the ICES Statistical Rectangles is available here.
Danvers is a village in McLean County, Illinois, United States. The population was 1,089 as of the 2020 census. It is part of the Bloomington–Normal Metropolitan Statistical Area.
Data and information visualization (data viz/vis or info viz/vis) is the practice of designing and creating graphic or visual representations of quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. These visualizations are intended to help a target audience visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data. When intended for the public to convey a concise version of information in an engaging manner, it is typically called infographics. Data visualization is concerned with presenting sets of primarily quantitative raw data in a schematic form, using imagery. The visual formats used in data visualization includes charts and graphs, geospatial maps, figures, correlation matrices, percentage gauges, etc.. Information visualization deals with multiple, large-scale and complicated datasets which contain quantitative data, as well as qualitative, and primarily abstract information, and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their cognition and help derive insights and make decisions as they navigate and interact with the graphical display. Visual tools used include maps for location based data; hierarchical organisations of data; displays that prioritise relationships such as Sankey diagrams; flowcharts, timelines. In addition, Narrative visualization is a method that uses visual elements such as charts, graphs, maps and the like to convey data or information. This approach blends data analysis, storytelling, and visualization to present information through structured narrative flows- using visuals not just to present statistics, but to tell a story through data. Its goal is to help people identify trends, patterns, and relationships in data through a compelling story-driven visual experience. Emerging technologies like virtual, augmented and mixed reality have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's visual perception and cognition. In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from databases, information systems, file systems, documents, business data, which is different from scientific visualization, where the goal is to render realistic images based on physical and spatial scientific data to confirm or reject hypotheses. Effective data visualization is well-sourced, appropriately contextualized, and presented in a simple, uncluttered manner. The underlying data is accurate and up-to-date to ensure insights are reliable. Graphical items are well-chosen and aesthetically appealing, with shapes, colors and other visual elements used deliberately in a meaningful and non-distracting manner. The visuals are accompanied by supporting texts. Verbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and expertise level of the target audience. Effective visualization can be used for conveying specialized, complex, big data-driven ideas to a non-technical audience in a visually appealing, engaging and accessible manner, and domain experts and executives for making decisions, monitoring performance, generating ideas and stimulating research. Data scientists, analysts and data mining specialists use data visualization to check data quality, find errors, unusual gaps, missing values, clean data, explore the structures and features of data, and assess outputs of data-driven models. Data and information visualization can be part of data storytelling, where they are paired with a narrative structure, to contextualize the analyzed data and communicate insights gained from analyzing it to convince the audience into making a decision or taking action. This can be contrasted with statistical graphics, where complex data are communicated graphically among researchers and analysts to help them perform exploratory data analysis or convey results of such analyses, where visual appeal, capturing attention to a certain issue and storytelling are less important. Data and information visualization is interdisciplinary, it incorporates principles found in descriptive statistics, visual communication, graphic design, cognitive science and, interactive computer graphics and human-computer interaction. Since effective visualization requires design skills, statistical skills and computing skills, it is both an art and a science. Visual analytics combines statistical data analysis, data and information visualization, and human analytical reasoning through interactive visual interfaces to help users reach conclusions, gain actionable insights and make informed decisions which are otherwise difficult for computers to do. Research into how people read and misread types of visualizations helps to determine what types and features of visualizations are most understandable and effective. Unintentionally poor or intentionally misleading and deceptive visualizations can function as powerful tools which disseminate misinformation, manipulate public perception and divert public opinion. Thus data visualization literacy has become an important component of data and information literacy in the information age akin to the roles played by textual, mathematical and visual literacy in the past.
The International Council for the Exploration of the Sea (ICES; French: Conseil International de l'Exploration de la Mer, CIEM) is a regional fishery advisory body and the world's oldest intergovernmental science organization. ICES is headquartered in Copenhagen, Denmark, where its multinational secretariat staff of 51 provide scientific, administrative and secretarial support to the ICES community. It was established on July 22, 1902, in Copenhagen.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Joe Louis.