Special Plan Areas:Main Street Historic DistrictHistoric DistrictDowntown PlanBusiness Improvement DistrictCore Area Plan

Níže uvedená zpráva obsahuje poznatky odvozené z údajů o maloobchodních výdajích společnosti Mastercard od High Streets Data Service. Data byla agregována v celém Londýně a podle typu města, aby poskytla přehled o výdajových trendech v těchto oblastech.

Všechna data jsou agregována a anonymizována z Mastercard.

Data byla agregována v celém Londýně a podle typu města, aby poskytla přehled o výdajových trendech v těchto oblastech.

Všechna data jsou agregována a anonymizována z Mastercard.

According to INSPIRE transformed development plan “8.3.2 Hohe Straße — Substation 2. Plan change” of the city of Ladenburg based on an XPlanung dataset in version 5.0.

Description of the INSPIRE Download Service (predefined Atom): Extended high street development plan, Lieser - The link(s) for downloading the data sets is/are dynamically generated from Get Map calls to a WMS interface

The development plan (BPL) contains the legally binding settlements for the urban planning order. In principle, the development plan must be developed from the land use plan. The available data is the development plan “High Street Residential Area” of the city of Filderstadt from XPlanung 5.0. Description: High street residential area; Usage: MI.

There are over 400 service requests types that are reported in the 311 system that affect the quality of life of our citizens, neighborhoods, and communities. The most popular service requests include but are not limited to animal services requests, high weeds, junk motor vehicles, and a number of other code compliance-related issues. Requests that deal with streets and mobility such as street and pot hole repair are also very common. 311 also receives requests to address environmental issues such as water conservation and air quality complaints. This dataset represents all Service Request from October 1, 2018 to present.

Graph and download economic data for High-Propensity Business Applications: Other Services in the United States (BAHBANAICS81NSAUS) from Jul 2004 to Jun 2025 about high-propensity, business applications, business, services, and USA.

The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

According to INSPIRE transformed development plan “Construction plan between main street and cemetery street” of the city of Gaggenau based on an XPlanung dataset in version 5.0.

Description of INSPIRE Download Service (predefined Atom): Building plan “High Road 6. Change cover sheet 6” of the city of Diez – The link(s) for downloading the records is/are generated dynamically from Get Map Calling a WMS Interface

This essential dataset is tailored for real estate investors, home service providers, and Proptech companies, offering in-depth information that drives strategic decision-making and market analysis for Property Owner Data.

The dataset includes detailed address data, owner data, and mailing address data, providing a thorough understanding of each property’s profile. Real estate investors can leverage this data to identify high-potential investment opportunities and analyze market trends with greater accuracy. Home service providers can utilize the mailing address data to target specific properties and optimize their outreach efforts. For Proptech companies, this dataset enhances the development of innovative solutions and data-driven platforms.

Powered by BatchData, a leader in high-quality, up-to-date property information, this dataset ensures you receive the most accurate and current data available. Explore BatchService’s USA Property Owner Data to gain a competitive edge and make informed decisions in the dynamic real estate market.

Basic Property Data Includes: - Property ID - Address City - Address County - Address County FIPS Code - Address Hash - Address House Number - Address Latitude - Address Longitude - Address State - Address Street - Address Zip - Address Zip+4 Code - APN (Assessor's Parcel Number) - Property Owner Full Name - Property Owner First Name - Property Owner Middle Name - Property Owner Last Name - Property Owner Mailing Address City - Property Owner Mailing Address County - Property Owner Mailing Address State - Property Owner Mailing Address Street - Property Owner Mailing Address Zip - Property Owner Mailing Address Zip+4 code

BatchService also has 700+ additional datapoints available ranging from listing information, property characteristics, mortgage data, contact information and more.

Abstract copyright UK Data Service and data collection copyright owner. The aims of the survey were to study the behaviour of shoppers in two North London shopping centres - Wood Green shopping centre and Brent Cross shopping centre, and to understand the motivations and desires of consumers. Main Topics: Relationship between shopping and identity; local shopping in North London; likes and dislikes of shopping; feedback on design of the shopping centres; time and money spent and activities whilst at shopping centres; who people like shopping with (families, friends, etc.); perceptions of shopping as an activity and views of shopping centres; frequency of use of high street, shopping centre and market in the locality; differences between shopping on the high street and shopping at malls. Quasi-random (eg random walk) sample Face-to-face interview

A. SUMMARY This data was created by the San Francisco Department of Public Health (SFDPH) to update the 2017 Vision Zero High Injury Network dataset. It identifies street segments in San Francisco that have a high number of fatalities and severe injuries. This dataset is a simplified representation of the network and only indicates which streets qualified; it does not contain any additional information, including prioritization by mode or a breakdown of count reported/unreported severe/fatal injuries by corridorized segment. SFDPH shares this network with CCSF agencies to help inform where interventions could save lives and reduce injury severity.

B. HOW THE DATASET IS CREATED The 2022 Vision Zero High Injury Network is derived from 2017-2022 severe and fatal injury data from Zuckerberg San Francisco General (ZSFG), San Francisco Police Department (SFPD), the Office of the Medical Examiner (OME), and Emergency Medical Services agencies. ZSFG patient records and SFPD victim records were probabilistically linked through the Transportation Injury Surveillance System (TISS) using LinkSolv Software. Injury severity for linked SFPD/ZSFG records was reclassified based on injury outcome as determined by ZSFG medical personnel (net 1732 police reported severe injuries) consistent with the Vision Zero Severe Injury Protocol (2017) while unlinked SFPD victim records were not changed (178 police reported severe injuries). Severe injuries captured by ZSFG but not reported to SFPD were also included in this analysis (650 unreported/unlinked geocodable severe injury patient records). Fatality data came from OME records that meet San Francisco’s Vision Zero Fatality Protocol (129 fatalities). Only transportation-related injuries resulting in a severe injury or fatality were used in this analysis. Each street centerline segment block was converted into ~0.25 mile overlapping corridorized sections using ArcPy. These sections were intersected with the severe/fatal injury data. Only severe/fatal injuries with the same primary street as the corridorized section were counted for that section. The count of severe/fatal injuries was then normalized by the sections mileage to derive the number of severe/fatal injuries per mile. A threshold of ≥10 severe/fatal injuries per mile was used as the threshold to determine if a corridorized segment qualified for inclusion into the network. A full methodology of the 2022 update to the Vision Zero High Injury Network can be found here: https://www.visionzerosf.org/wp-content/uploads/2023/03/2022_Vision_Zero_Network_Update_Methodology.pdf

C. UPDATE PROCESS This dataset will be updated on an as needed basis.

D. HOW TO USE THIS DATASET The 2022 Vision Zero Network represents a snapshot in time (2017-2021) where severe and fatal injuries are most concentrated. It may not reflect current conditions or changes to the City’s transportation system. Although prior incidents can be indicative of future incidents, the 2022 Vision Zero High Injury Network is not a prediction (probability) of future risk. The High Injury Network approach is in contrast to risk-based analysis, which focuses on locations determined to be more dangerous with increased risk or danger often calculated by dividing the number of injuries or collisions by vehicle volumes to estimate risk of injury per vehicle. The High Injury Network provides information regarding the streets where injuries, particularly severe and fatal, are concentrated in San Francisco based on injury counts; it is not an assessment of whether a street or particular location is dangerous. The 2022 Vision Zero Network is derived from the more severe injury outcomes (count of severe/fatal injuries) and may not cover locations with high numbers of less severe injury collisions. Hospital and emergency medical service records from which SFPD-unreported injury and reclassified injury collisions are derived are protected by the Health Insurance Portability and Accountability Act and state medical privacy laws, thus have strict confidentiality and privacy requirements. As of November 2021, SFDPH is working in conjunction with SFDPH’s Office of Compliance and Privacy Affairs, Zuckerberg San Francisco General Hospital (“ZSFG”) and the SFMTA to determine how SFDPH can share the data in compliance with federal and state privacy laws. Intersection and other small-area specific counts of severe/fatal injuries have thus been intentionally excluded from this document as data sharing requirements are yet to be determined.

E. RELATED DATASETS