993 resultados para Hudson County


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This layer is a georeferenced raster image of the historic paper map entitled: Map of Hillsboro Co., New Hampshire, from actual surveys by J. Chace, Jr. It was published by Smith, Mason & Co. in 1858. Scale [ca. 1:53,000]. This layer is image 3 of 4 total images, representing the southeast portion of the four sheet source map. The image inside the map neatline is georeferenced to the surface of the earth and fit to the New Hampshire State Plane coordinate system (NAD 1983 in Feet) (Fipszone 2800). All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as roads, railroads, drainage, public buildings, schools, churches, cemeteries, industry locations (e.g. mills, factories, mines, etc.), private buildings with names of property owners, town and school district boundaries, and more. Relief shown by hachures. Includes table of distances, agricultural goods, religious affiliations, business directory, statistics of education and other information, and a list of county officers.This layer is part of a selection of digitally scanned and georeferenced historic maps of New England from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of regions, originators, ground condition dates, scales, and map purposes.

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This layer is a georeferenced raster image of the historic paper map entitled: Map of Middlesex County, Massachusetts, the details from original surveys under the direction of Henry F. Walling, supt. of the state map ; Thos. W. Baker, draughtsman. It was published by Smith & Bumstead in 1856. Scale 1:50,000. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Massachusetts State Plane Coordinate System, Mainland Zone (in Feet) (Fipszone 2001). All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, or other information associated with the principal map. This map shows features such as roads, railroads, drainage, public buildings, schools, churches, cemeteries, industry locations (e.g. mills, factories, mines, etc.), private buildings with names of property owners, town and county boundaries and more. Covers also parts of Boston. Relief is shown by hachures. It includes many cadastral insets of individual county towns and villages, and an inset geological map of county. It also includes illustrations, business directories, and tables of statistics and distances. This layer is part of a selection of digitally scanned and georeferenced historic maps of Massachusetts from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of regions, originators, ground condition dates (1755-1922), scales, and purposes. The digitized selection includes maps of: the state, Massachusetts counties, town surveys, coastal features, real property, parks, cemeteries, railroads, roads, public works projects, etc.

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Cover title.

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"This report was financed in part by a grant from the U.S. Environmental Protection Agency under section 314 of the Clean Water Act."

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A later edition, New York, 1849, published under title: The border warfare of New York, during the revolution; or, The annals of Tryon county.

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Hudson Mills, 1908: Map of the Huron River Valley [Scanned in two parts and combined using PhotoShop CS6 PhotoMerge command]

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Between Dexter and Hudson Mills, 1908: Map of the Huron River Valley [Scanned in two parts and combined using PhotoShop CS6 PhotoMerge command]

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Between Dexter and Hudson Mills, 1908: Map of the Huron River Valley [Scanned in two parts and combined using PhotoShop CS6 PhotoMerge command]

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China has experienced an extraordinary level of economic development since the 1990s, following excessive competition between different regions. This has resulted in many resource and environmental problems. Land resources, for example, are either abused or wasted in many regions. The strategy of development priority zoning (DPZ), proposed by the Chinese National 11th Five-Year Plan, provides an opportunity to solve these problems by coordinating regional development and protection. In line with the rational utilization of land, it is proposed that the DPZ strategy should be integrated with regional land use policy. As there has been little research to date on this issue, this paper introduces a system dynamic (SD) model for assessing land use change in China led by the DPZ strategy. Land use is characterized by the prioritization of land development, land utilization, land harness and land protection (D-U-H-P). By using the Delphi method, a corresponding suitable prioritization of D-U-H-P for the four types of DPZ, including optimized development zones (ODZ), key development zones (KDZ), restricted development zones (RDZ), and forbidden development zones (FDZ) are identified. Suichang County is used as a case study in which to conduct the simulation of land use change under the RDZ strategy. The findings enable a conceptualization to be made of DPZ-led land use change and the identification of further implications for land use planning generally. The SD model also provides a potential tool for local government to combine DPZ strategy at the national level with land use planning at the local level.

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Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.