Difficult Development Areas (DDA) for the Low Income Housing Tax Credit program are designated by U.S. Department of Housing and Urban Development (HUD) and defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI). DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs in non-metropolitan areas are designated along county boundaries. DDAs may not contain more than 20% of the aggregate population of metropolitan and non-metropolitan areas, which are designated separately. To learn more about Difficult Development Areas (DDA) visit: https://www.huduser.gov/portal/datasets/qct.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Difficult Development Areas Date of Coverage: 2024-2025Last Updated: 01-2025

Difficult Development AreasThis U.S. Department of Housing and Urban Development feature layer depicts Difficult Development Areas in the United States. Per HUD, "Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data." All DDA's in Metropolitan Statistical Areas (MSA) and Primary Metropolitan Statistical Areas (PMSA) may not contain more than 20% of the aggregate population of all MSA's/PMSA's, and all designated areas not in metropolitan areas may not contain more than 20% of the aggregate population of the non-metropolitan counties.Baltimore/Columbia/Towson Small Area DDAData currency: Current Federal ServiceData modification: NoneFor more information: Housing and Urban Development; Qualified Census Tracts and Difficult Development AreasFor feedback, please contact: ArcGIScomNationalMaps@esri.comDepartment of Housing and Urban DevelopmentPer HUD, "The Department of Housing and Urban Development administers programs that provide housing and community development assistance. The Department also works to ensure fair and equal housing opportunity for all."

This dataset provides data on Qualified Census Tracts for the Low-Income Housing Tax Credit Program for 2024. LIHTC Qualified Census Tracts, as defined under the section 42(d)(5)(C) of the of the Internal Revenue Code of 1986, include any census tract (or equivalent geographic area defined by the Bureau of the Census) in which at least 50 percent of households have an income less than 60 percent of the Area Median Gross Income (AMGI), or which has a poverty rate of at least 25 percent. Maps of Qualified Census Tracts and Difficult Development Areas are available at: huduser.gov/sadda/sadda_qct.html .

Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are designated by the U.S. Department of Housing and Urban Development and are based on Fair Market Rents, income limits, the 2010 census counts, and 2006–10 5-year American Community Survey data when they becomes available. Beginning with the 2016 DDA designations, metropolitan DDAs will use Small Area Fair Market Rents (FMRs) rather than metropolitan-area FMRs for designating metropolitan DDAs. Maps of Qualified Census Tracts and Difficult Development Areas are available at: huduser.gov/sadda/sadda_qct.html. This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/BusinessEconomy/MD_HousingDesignatedAreas/FeatureServer/3

This service contains a list of census tracts that qualify for the American Rescue Plan Act (ARPA) . The list was provided to EGIS by BMS. The data used to produce this service can be found at Qualified Census Tracts and Difficult Development Areas | HUD USER.Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data. Maps of Qualified Census Tracts and Difficult Development Areas are available at: 2023 and 2024 Small DDAs and QCTs | HUD USER.Qualified Census Tracts - Generate QCT Tables for Individual Areas (Also Includes DDA Information)This data was created by the Department of Housing and Urban Development in 2023. This data is updated on a yearly basis.

A feature service prepared by U.S. Department of Housing and Urban Development (U.S. HUD) that displays Difficult Development Areas (DDA) for the Low Income Housing Tax Credit program. DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI).Date of Coverage: 2021 Data Updated: Annually

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants. This dataset includes the fastq files provided to participants, the submitted variant callset as vcfs, and the benchmarking results, along with challenge submission metadata.

This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.Layer includes percent of population with a disability categorized as:an independent living difficultya hearing difficultyan ambulatory difficultya vision difficultya cognitive difficultya selfcare difficultyA ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: data.census.gov

Baton Rouge's unique past has shaped the city that we live in today. The layout of the city's streets, the arrangement of prominent government and religious structures, the clustering of businesses, the distribution of residential neighborhoods, and the placement of parks and schools all speak to the long term processes of urban growth. Society invests tremendous effort in creating its urban centers and citizens develop attachments to those places. It is the investment of human effort that stimulates a sense of place and allows individuals to develop strong feelings about their home city. Sense of place is constantly reinforced by contact with the common, everyday landscapes that surround us. In Baton Rouge, the two principal university campuses, the state government complex, along with various historic neighborhoods and structures all stand as perpetual reminders of the city's past. Many familiar and, at the same time, unique landscape features of Baton Rouge shape our sense of place. Much has been written about the distinctive buildings that come to mind when Baton Rouge is mentioned, but what of the larger districts and neighborhoods? Residents generally are most familiar with their immediate surroundings or those places where they work and play and these surroundings ofter constitute more than a building or two. Children comprehend their immediate neighborhoods and those who move about a city come to know and develop ideas about the city's larger units. Geographers and planners like to think of cities in terms of these larger assemblages

This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.Layer includes percent of population with a disability categorized as:an independent living difficultya hearing difficultyan ambulatory difficultya vision difficultya cognitive difficultya selfcare difficultyA ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2019-2023ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 12, 2024National Figures: data.census.gov

Local, state, tribal, and federal agencies use disability data to plan and fund programs for people with disabilities. Disability data helps communities enroll eligible households in programs designed to assist them such as health care programs and affordable housing programs. Disability data also helps local jurisdictions provide services that:Enable older adults to remain living safely in their homes and communities (Older Americans Act).Provide services and assistance to people with a disability, such as financial assistance with utilities (Low Income Home Energy Assistance Program)Disability data helps communities qualify for grants such as the Community Development Block Grant (CDBG) Program, the HOME Investment Partnership Program, the Emergency Solutions Grants (ESG) Program, the Housing Opportunities for Persons with AIDS (HOPWA) Program, and other local and federal programs.Disability data are also used to evaluate other government programs and policies to ensure that they fairly and equitably serve the needs of all groups, as well as enforce laws, regulations, and policies against discrimination.This map shows the count and prevalence of people with a disability. This includes people with a hearing difficulty, a vision difficulty, an ambulatory difficulty, a cognitive difficulty, a self-care difficulty, and an independent-living difficulty. The features in web map are symbolized using color and size to depict total population with a disability count (size of symbol) and prevalence (color of symbol). Web map is multi-scaled, and opens displaying data for counties and tracts. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Biogeoclimatic Ecosystem Classification (BEC) system is the ecosystem classification adopted in the forest management within British Columbia based on vegetation, soil, and climate characteristics whereas Site Series is the smallest unit of the system. The Ministry of Forests, Lands, Natural Resource Operations and Rural Development held under the Government of British Columbia (“the Ministry”) developed a web-based tool known as BEC Map for maintaining and sharing the information of the BEC system, but the Site Series information was not included in the tool due to its quantity and complexity. In order to allow users to explore and interact with the information, this project aimed to develop a web-based tool with high data quality and flexibility to users for the Site Series classes using the “Shiny” and “Leaflet” packages in R. The project started with data classification and pre-processing of the raster images and attribute tables through identification of client requirements, spatial database design and data cleaning. After data transformation was conducted, spatial relationships among these data were developed for code development. The code development included the setting-up of web map and interactive tools for facilitating user friendliness and flexibility. The codes were further tested and enhanced to meet the requirements of the Ministry. The web-based tool provided an efficient and effective platform to present the complicated Site Series features with the use of Web Mapping System (WMS) in map rendering. Four interactive tools were developed to allow users to examine and interact with the information. The study also found that the mode filter performed well in data preservation and noise minimization but suffered from long processing time and creation of tiny sliver polygons.

The 342 communes of “Hérault,” are informed about their submission to the coastal or mountain law, their classification in mountain areas and their belonging to a massive perimeter. This layer is built after the Bd Carto 2012. Law “mountain” No 85-30 of 9 January 1985 as amended on the development and protection of mountains Article 3 of the Act: mountain areas are characterised by significant handicaps resulting in more difficult living conditions and restricting the exercise of certain economic activities. They include municipalities or parts of municipalities characterised by a considerable limitation of land use possibilities and a significant increase in the costs of works due to: * or to the existence, due to altitude, of very difficult climatic conditions [...] * the presence, at a lower altitude, [...], of steep slopes [...] * the combination of these two factors... The massif encompasses not only mountain areas, but also areas immediately adjacent to them: foothills or even plains if the latter ensure the continuity of the massif. The concept of massif is a French-only approach, allowing an administrative entity competent to carry out mountain policy, but to differentiate from the concept of mountain.

These rasters provide the local mean annual extreme low temperature from 1976 to 2005 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands. The classified rasters are then used to create print and interactive maps. A complex algorithm was used for this edition of the USDA Plant Hardiness Zone Map (PHZM) to enable more accurate interpolation between weather reporting stations. This new method takes into account factors such as elevation changes and proximity to bodies of water, which enabled mapping of more accurate zones.Temperature station data for this edition of the USDA PHZM came from several different sources. In the eastern and central United States, Puerto Rico, and Hawaii, nearly all the data came from weather stations of the National Weather Service. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Global Historical Climate Network.All of these data were carefully examined to ensure that only the most reliable were used in the mapping. In the end, data from a total of 7,983 stations were incorporated into the maps. The USDA PHZM was produced with the latest version of PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half a mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom).Information on PRISM can be obtained from the PRISM Climate Group website (http://prism.oregonstate.edu).Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked for errors or biases.For example, zones along the Canadian border in the Northern Plains initially appeared slightly too warm to several members of the review team who are experts in this region. It was found that there were very few weather reporting stations along the border in the United States in that area. Data from Canadian reporting stations were added, and the zones in that region are now more accurately represented. In another example, a reviewer noted that areas along the relatively mild New Jersey coastline that were distant from observing stations appeared to be too cold. This was remedied by increasing the PRISM algorithm’s sensitivity to coastal proximity, resulting in a mild coastal strip that is more consistently delineated up and down along the shoreline.On the other hand, a reviewer familiar with Maryland’s Eastern Shore thought the zones there seemed too warm. The data were doublechecked and no biases were found; the zone designations remained unchanged.The zones in this edition were calculated based on 1976-2005 temperature data. Each zone represents the average annual extreme minimum temperature for an area, reflecting the temperatures recorded for each of the years 1976-2005. This does not represent the coldest it has ever been or ever will be in an area, but it reflects the average lowest winter temperature for a given geographic area for this time period. This average value became the standard for assigning zones in the 1960s. The previous edition of the USDA Plant Hardiness Zone Map, which was revised and published in 1990, was drawn from weather data from 1974 to 1986.A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the new PHZM are available from the articles listed below.Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264. Link to articleWidrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19. Link to article

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Abstract: Old growth forest mapping from aerial photograph interpretation of canopy species regeneration and senescent growth stages. Scale 1:25,000. Bounded by NSW Morriset Forestry District. Boundaries include the New England Highway and Hunter River in the North,the Blue mountains and Wollemi National Park in the west and the Illawarra highway in the south. VIS_ID 4122

Purpose

To map old growth forest in the Morriset area.

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Digitised on screen over 1:250,000 scale topographic maps. Attributes were verified in the field and by NSW state forests. Stereoscopic interpretation using a range of stereoscopes with a variety of magnifications (eg. Topcon and Abrams stereoscopes with x10 magnification). Attributes table & codes - Information contained in the attribute table is: regrowth-juvenile and sapling, regrowth-pole, Mature-early mature & mature, senescent-late mature and overmature, disturbance code. The project was only concerned with pyrophytic vegetation consequently, vegetation that was <10% pyrophytic was coded with an O and rainforest was coded with an R. Mapping pathway - An api pathway was developed specifically for the BOGMP. O =0%.if vegetation obviously rainforest= R 1 =0-10%,if vegetetation obviously rainforest=R 2 =11-20% and 3 = 21-30% If understorey rainforest rather than grass or heath. On ecological grounds this should be called rainforest but there might be some debate over the upper part of class 3.= R 4 = 31-50% difficult to see the understorey, would require ground truthing. Could have rainforest in which case there would be an argument about whether to be treated as a separate vegetation type or as a serial stage of rainforest Discretionary(presence/absence of rainforest understorey) 5 =51-60% Could have rainforest elements but difficult to determine as rainforest. Eucalypt = 81-100% unlikely to be rainforest in understorey. Eucalypt The mapping pathway specified that in eucalypt forest, primary polygon primary polygon delineation was based on floristics then split firstly on structural differences, structure (% regrowth and senescence), secondly on height classes then regrowth size class, and tagged for relative stand density and disturbance indicators. No senescence was recorded for polygons outside of State Forest with >30% regrowth. Eucalyptus-dominated vegetation with <20% ccp was not delineated. Data recording. The aerial photographs were pre-prepared and supplied to interpreters with Effective areas and land tenure boundaries marked directly on to the photographs. The effective area of a photograph included all images closer to the centre of the photograph than to the centre of any other. The central old growth study area used recent logging disturbance maps provided by state forests. Land Cover - Vegetation Cover greater than 20% canopy cover Floristics - Classification into pyrophytic vegetation, <10% pyrophytic vegetation, and rainforest Strata - Mapping pathway delineates a code of rainforest or eucalypt according to understorey type in areas with discrepencies Growth Stage - Regeneration and senescence Multi-attribute Mapping - Native vegetation greater than 20% ccp delineated. Relative stand density for the regrowth component of the vegetation also identified. Special features identified (eg. exotic pine plantation). Land use / cover not identified. Survey Type point to plant transects. Inaccessible areas were assessed using aircraft. Information Collected Growth stage, disturbance and vegetation assessment Date of surveys -1996? Minimum Polygon Size -25 hectares Edge Matching - Not assessed Polygon Attribution - Comparison of the growth stage polygon codes and linework against a hard copy map and against the original linework on the aerial photographs.. Both a 10% random sample of the photographs,and all the photographs in a specific area were checked for coding and linework errors. Custodian - NPWS Date of map product -1996 Strengths - A validation process was implemented. Detailed growth stage information and disturbance information. Field checking was undertaken. Multi-attribute mapping with broad geographic coverage, relatively high quality data capture techniques, Weaknesses - The difference in ability of the interpreters (eg moist, high site quality forest types were more reliably mapped than other forest types).Field work was insufficient, confined to state forest tenure. High possibility of post mapping logging and disturbance.

Dataset Citation

NSW Office of Environment and Heritage (2015) Old Growth Forest Mapping Broad, Central, 1996. VIS_ID 4122 2015 20150116. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/85a296b9-0c03-4dec-a0c1-cb22debbdbd1.

Business Mapping Software Market Outlook

The global business mapping software market size was valued at $3.5 billion in 2023 and is projected to reach $6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% during the forecast period. This growth can be attributed to the increasing adoption of advanced analytical tools, the growing need for efficient territory management, and the rising demand for location-based intelligence in various business operations.

One of the primary growth factors propelling the business mapping software market is the rising need for visualization tools that can transform complex data sets into actionable insights. Businesses across various sectors are increasingly adopting these tools to enhance their decision-making processes. The ability to integrate mapping software with other enterprise applications such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems has further driven its adoption. This integration allows businesses to visualize customer data, sales trends, and supply chain logistics on a geographical map, thereby facilitating more informed strategic decisions.

Another significant growth factor is the increasing demand for effective territory management solutions. As companies expand their operations geographically, the challenge of managing sales territories, distribution networks, and service areas becomes more complex. Business mapping software helps organizations optimize their territory alignment, ensuring balanced workload distribution and improved sales performance. This is particularly crucial for companies with large field sales forces, as efficient territory management directly impacts revenue generation and customer satisfaction.

The growing importance of location-based analytics has also contributed to the market's expansion. Businesses are leveraging mapping software to gain insights into demographic trends, market potential, and competitive landscape. By overlaying business data with geographic information, companies can identify emerging market opportunities, optimize their marketing strategies, and enhance supply chain efficiency. This trend is particularly evident in industries such as retail, healthcare, and logistics, where location intelligence plays a pivotal role in operational planning and execution.

Regionally, North America holds a significant share of the business mapping software market due to the early adoption of advanced technologies and the presence of major market players. The Asia Pacific region, however, is expected to witness the highest growth rate during the forecast period, driven by rapid economic development, increasing digitalization, and the growing awareness of the benefits of business mapping solutions. Europe also represents a substantial market share, with a strong emphasis on data-driven decision-making across various industries.

Service Mapping is becoming increasingly vital in the realm of business mapping software, as organizations strive to enhance their operational efficiency and customer service. By leveraging service mapping tools, businesses can gain a comprehensive understanding of their service delivery networks, enabling them to identify gaps, streamline processes, and improve service quality. This capability is particularly beneficial for industries such as healthcare and telecommunications, where efficient service delivery is crucial for maintaining customer satisfaction and competitive advantage. Service mapping allows organizations to visualize and analyze their service territories, ensuring optimal resource allocation and effective coverage. As the demand for personalized and efficient services continues to grow, service mapping will play a pivotal role in helping businesses meet these expectations and drive growth.

Component Analysis

The business mapping software market is segmented by component into software and services. The software segment dominates the market, driven by continuous advancements in software capabilities, including enhanced user interfaces, improved data integration, and sophisticated analytical tools. The development of user-friendly, cloud-based solutions has made it easier for businesses of all sizes to adopt these technologies, further fueling market growth. Moreover, the increasing need for real-time data visualization and the ability to generate customized maps for various business needs have expanded the software segment's reach.

Map Drawing Services Market Outlook

The global map drawing services market size was valued at approximately $1.2 billion in 2023 and is projected to reach $2.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% during the forecast period. This growth can be attributed to the increasing demand for precise and customized mapping solutions across various industries such as urban planning, environmental management, and tourism.

One of the primary growth factors of the map drawing services market is the rapid advancement in Geographic Information Systems (GIS) technology. The integration of advanced GIS tools allows for the creation of highly accurate and detailed maps, which are essential for urban planning and environmental management. Additionally, the growing emphasis on smart city initiatives worldwide has led to an increased need for customized mapping solutions to manage urban development and infrastructure efficiently. These technological advancements are not only improving the quality of map drawing services but are also making them more accessible to a broader range of end-users.

Another significant growth factor is the rising awareness and adoption of map drawing services in the tourism sector. Customized maps are increasingly being used to enhance the tourist experience by providing detailed information about destinations, routes, and points of interest. This trend is particularly prominent in regions with rich cultural and historical heritage, where detailed thematic maps can offer tourists a more immersive and informative experience. Furthermore, the digitalization of the tourism industry has made it easier to integrate these maps into various applications, further driving the demand for map drawing services.

Environmental management is another key area driving the growth of the map drawing services market. With the increasing focus on sustainable development and environmental conservation, there is a growing need for accurate maps to monitor natural resources, track changes in land use, and plan conservation efforts. Map drawing services provide essential tools for environmental scientists and policymakers to analyze and visualize data, aiding in better decision-making and management of natural resources. The rising environmental concerns globally are expected to continue driving the demand for these services.

From a regional perspective, North America is anticipated to hold a significant share of the map drawing services market due to the high adoption rate of advanced mapping technologies and the presence of major market players in the region. Furthermore, the region's focus on smart city projects and environmental conservation initiatives is expected to fuel the demand for map drawing services. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate, driven by rapid urbanization, industrialization, and the growing need for efficient infrastructure planning and management.

Service Type Analysis

The map drawing services market is segmented into several service types, including custom map drawing, thematic map drawing, topographic map drawing, and others. Custom map drawing services cater to specific client needs, offering tailored mapping solutions for various applications. This segment is expected to witness significant growth due to the increasing demand for personalized maps in sectors such as urban planning, tourism, and corporate services. Businesses and government agencies are increasingly relying on custom maps to support their operations, leading to the expansion of this segment.

Thematic map drawing services focus on creating maps that highlight specific themes or topics, such as population density, climate patterns, or economic activities. These maps are particularly useful for educational purposes, research, and community planning. The growing emphasis on data-driven decision-making and the need for visual representation of complex datasets are driving the demand for thematic maps. Additionally, thematic maps play a crucial role in public health, disaster management, and policy formulation, contributing to the segment's growth.

Topographic map drawing services offer detailed representations of physical features of a landscape, including elevation, terrain, and landforms. These maps are essential for various applications, such as environmental management, military ope

With the resolution revolution of cryo-electron microscopy (cryo-EM) and the rapid development of image processing technology, cryo-EM has become an indispensable experimental method for determining the three-dimensional structures of biological macromolecules. However, structural modeling from cryo-EM maps remains a difficult task for intermediate-resolution maps. In such cases, detection of protein secondary structures and nucleic acid locations in an EM map is of great value for model building of the map. Meeting the need, we present a deep learning-based method for detecting protein secondary structures and nucleic acid locations in cryo-EM density maps, named EMInfo. EMInfo was extensively evaluated on two protein-nucleic acid complex test sets including intermediate-resolution experimental maps and high-resolution experimental maps and compared them with two state-of-the-art methods including Emap2sec+ and Haruspex. It is shown that EMInfo can accurately predict different structural categories in an EM map. EMInfo is freely available at http://huanglab.phys.hust.edu.cn/EMInfo/.

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend. This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application. NOTE: To see this entire product in the map viewer, use a base map in the "World" section (EPSG: 3857).

Law “mountain” No 85-30 of 9 January 1985 as amended on the development and protection of mountains Article 3 of the Law: mountain areas are characterised by significant handicaps resulting in more difficult living conditions and restricting the exercise of certain economic activities. They include municipalities or parts of municipalities characterised by a considerable limitation of land use possibilities and a significant increase in the costs of works due to: — the existence, due to altitude, of very difficult climatic conditions [...]

Order of 6 September 1985: OJ of 18/09/1985, page 10713 — the presence, at a lower altitude, [...] of steep slopes... — either the combination of these two factors...