Counts of Part I committed in San Mateo County from 1985 on. This dataset also includes Part II crimes from 2013 on.

Part I crimes include: homicide, rape, robbery, aggravated assault, burglary, motor vehicle theft, larceny-theft, and arson. These counts include crimes committed at San Francisco International Airport (SFO), Unincorporated San Mateo County, Woodside, Portola Valley, San Carlos from 10/31/10 forward; Half Moon Bay from 6/12/11 forward; and Millbrae from 3/4/12 forward.

Part II crimes do not include San Francisco International Airport (SFO) cases and is an estimate only. An estimate is required because there are no specific data types used when keying in Type II crime types. Therefore, Records Manager judgment is used.

Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

This horizontal bar chart displays urban land area (km²) by region using the aggregation sum in the Americas. The data is about regions.

The bar graph array market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market sizing data is unavailable, a reasonable estimate based on industry trends and comparable technologies suggests a 2025 market value of approximately $500 million. This reflects a substantial expansion from the estimated $300 million in 2019, indicating a healthy Compound Annual Growth Rate (CAGR) exceeding 10%. Key drivers include the rising adoption of smart displays in consumer electronics, industrial automation's reliance on precise visual feedback, and the growing popularity of automotive instrument clusters incorporating advanced display technologies. Furthermore, advancements in LED technology, miniaturization, and improved energy efficiency are contributing to market expansion. The segment exhibiting the fastest growth is likely the automotive sector, due to the increasing integration of sophisticated driver information systems and in-car entertainment. However, competitive pricing pressure from Asian manufacturers and the need for continuous technological innovation represent key restraints. Major players in this dynamic market include established companies like Broadcom, London Electronics Limited, and others mentioned. These companies are actively engaged in product development and strategic partnerships to maintain a competitive edge. The market is further segmented by application (automotive, industrial, consumer electronics, etc.) and geography, with North America and Europe representing significant regional markets, while Asia-Pacific is expected to exhibit substantial growth potential in the coming years due to a large and expanding consumer electronics market and increasing industrial automation initiatives. The forecast period (2025-2033) anticipates continued growth, fueled by technological advancements, emerging applications, and sustained demand across diverse end-use industries. This makes the bar graph array market an attractive area for investment and further technological development.

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This dataset contains data from nontargeted analysis of the OTM-45 extracts obtained during the incineration of PFAS containing AFFF. The data was used to create Figure 2 in the associated manuscript. The Figure uses the sums of the peak areas to create a bar graph show relative peak areas of fluorinated features for each of the incineration conditions studied. The peak areas for the features identified as PFAS that are targeted for typical analyses were plotted along with the total areas to show how many of the features fall outside of typical PFAS analyses. This dataset is associated with the following publication: Shields, E., J. Krug, W. Roberson, S. Jackson, M. Smeltz, M. Allen, R.(. Burnette, J. Nash, L. Virtaranta, W. Preston, H. Liberatore, A. Wallace, J. Ryan, P. Kariher, P. Lemieux, and B. Linak. Pilot‐Scale Thermal Destruction of Per‐ and Polyfluoroalkyl Substances in a Legacy Aqueous Film Forming Foam. ACS ES&T Engineering. American Chemical Society, Washington, DC, USA, 3(9): 1308–1317, (2023).

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This horizontal bar chart displays forest area (km²) by region using the aggregation sum. The data is about regions.

This horizontal bar chart displays forest area (km²) by continent using the aggregation sum. The data is about regions.

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This bar chart displays forest area (km²) by date using the aggregation sum in Hungary. The data is filtered where the date is 2021. The data is about countries per year.

This horizontal bar chart displays median age (year) by regions using the aggregation average, weighted by population. The data is about regions.

This horizontal bar chart displays urban land area (km²) by region using the aggregation sum in Europe. The data is about countries.

This bar chart displays urban land area (km²) by country full name using the aggregation sum in Southern Asia. The data is about countries.

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This bar chart displays urban land area (km²) by country using the aggregation sum in South America. The data is about countries.