Cellular Service Area Boundaries

Cellular service area boundaries by callsign

By Homeland Infrastructure Foundation [source]

About this dataset

The Cellular_Service_Areas.csv dataset provides detailed information about the boundaries and characteristics of cellular service areas. It categorizes these areas based on their respective callsigns, which are unique identifiers associated with each service area.

The dataset includes several columns that provide valuable information for analyzing cellular service areas. The Shape_Area column represents the area of each service area's shape or boundary, measured in numeric or float values. This allows researchers to understand the extent and size of each coverage zone.

The Shape_Leng column provides the length of the shape or boundary for each cellular service area. Like Shape_Area, it is also represented in numeric or float values and offers insight into the physical dimensions of these zones.

Another significant piece of data provided is the CALL column, which contains text or string values representing the callsign associated with each cellular service area. Callsigns serve as unique identifiers for allocating frequencies among different operators and can be indicative of specific mobile network providers.

Additionally, this dataset includes information about license holders in the LICENSEE column. This text/string field specifies the company or organization that holds a license for operating within a particular cellular service area. It allows users to identify and analyze ownership patterns within this industry.

By combining all these attributes, researchers can gain a comprehensive understanding of different aspects related to cellular service areas like their shapes, sizes, callsign associations, and licensee organizations. The data can enable various analyses such as coverage comparisons between different providers' zones or evaluating geographical distribution trends among license holders.

Overall, this dataset serves as a valuable resource for studying and mapping out cellular service areas categorized by callsigns to better comprehend their geographic reach and industry dynamics

How to use the dataset

1. Downloading the Dataset

You can download the dataset from here. Simply click on the link and select the file format that suits your requirements (e.g., CSV, XLSX).

2. Explore the Dataset

The dataset contains several columns providing valuable information about cellular service areas. Here is a brief description of each column:

• CALL: The callsign associated with the cellular service area.
• LICENSEE: The company or organization that holds the license for the cellular service area.
• Shape_Leng: The length of the shape or boundary of the cellular service area.
• Shape_Area: The area of the shape or boundary of the cellular service area.

By analyzing these columns, you can gain insights into different aspects related to cellular coverage areas across various companies and regions.

3. Importing and Loading Data

Once you have downloaded and saved your preferred file format from this dataset, you can import it into your data analysis tool (such as Python's Pandas library) using standard file reading functions specific to your tool.

For example, in Python using Pandas: ```python import pandas as pd

Load CSV file into a DataFrame

df = pd.read_csv('Cellular_Service_Areas.csv')

Explore data using various DataFrame operations

Make sure to adjust your code accordingly based on your chosen programming language and tools.

## 4. Analyzing and Visualizing Data

With the dataset loaded, you can start exploring and analyzing it to uncover meaningful insights. Here are a few potential analysis and visualization ideas:

- **Coverage Analysis**: Group the cellular service areas by **LICENSEE** to understand how different companies or organizations distribute their coverage across different regions.
- **Size Analysis**: Calculate statistics for **Shape_Area** column to identify the largest and smallest cellular service areas.
- **Length Analysis**: Analyze the information in the **Shape_Leng** column to determine variations in boundary lengths among different callsigns.
- **Geospatial Visualization**: Utilize geospatial libraries such as GeoPandas or GIS

Research Ideas

• Network Coverage Analysis: By analyzing the boundaries of cellular service areas, this dataset can be used to assess the coverage a...

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GIS In Telecom Sector Market Size 2024-2028

The gis in telecom sector market size is forecast to increase by USD 1.91 billion at a CAGR of 14.68% between 2023 and 2028.

The market is experiencing significant growth due to the increased adoption of Geographic Information Systems (GIS) for capacity planning and network optimization. Telecommunication companies are leveraging GIS technology to analyze and visualize data, enabling them to make informed decisions regarding network expansion, maintenance, and resource allocation. The integration of GIS with big data is a key trend driving market growth, as it allows for real-time analysis of vast amounts of data, leading to improved network performance and customer experience. However, the market is not without challenges. A communication gap exists between developers and end-users, making it essential for telecom companies to invest in user-friendly GIS solutions. This will require collaboration between developers and end-users to ensure that GIS applications meet the specific needs of the telecom industry. By addressing this challenge, companies can capitalize on the market's growth potential and effectively navigate the strategic landscape of the market.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

Request Free SampleIn the dynamic telecom sector, network intelligence plays a crucial role in driving efficiency and enhancing performance. Wireless network planning relies on key performance indicators to optimize network coverage and capacity. Network security audits ensure the protection of critical infrastructure, while service fulfillment utilizes geospatial databases and network monitoring systems. Telecom infrastructure planning incorporates data modeling and customer relationship management to streamline operations. Packet loss and network outage management are essential components of network availability and performance, with telecom billing systems and order management facilitating seamless business processes. Mobile mapping software and network modeling enable spatial data management, while data integration and data analysis provide valuable network insights. Telecom analytics platforms employ network simulation and capacity planning tools to optimize network performance and ensure network availability. Network optimization algorithms and network performance metrics offer valuable data-driven insights for service interruptions and network insights. Service area optimization and disaster recovery planning are essential elements of a robust telecom strategy, ensuring business continuity and resilience. Data visualization tools facilitate effective decision-making and enable organizations to maintain a competitive edge in the evolving telecom landscape.

How is this GIS In Telecom Sector Industry segmented?

The gis in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeItalyUKMiddle East and AfricaAPACChinaSouth AmericaRest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the telecom sector, the Global Geographic Information System (GIS) market plays a pivotal role in enhancing network performance and operational efficiency. The software segment, comprising GIS software for desktops, mobiles, cloud, and servers, as well as GIS developers' platforms, experiences significant growth due to the increasing adoption of industry-specific solutions. Telecom companies leverage intelligent maps generated by GIS for strategic decisions on network capacity planning, service improvement, and next-generation enhancements. Moreover, commercial companies provide open-source GIS software to counteract the proliferation of counterfeit products. Network resilience and asset management are crucial aspects of telecom infrastructure management, where GIS software offers valuable insights through location intelligence and spatial analysis. Machine learning and artificial intelligence technologies integrated into GIS software enable predictive network planning and automation, enhancing network reliability and quality. Network operators and service providers invest in wireless network design, 5G network deployment, and edge computing to improve customer experience and network performance. GIS software facilitates network capacity planning, coverage analysis, and tower planning, ensuring optimal network utilization and service level agreements. Network security and infrastructure management are integral components of telecom operations, where GIS software offers data visualization, geospatial data analysis, and

Article Information

The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.

Please do cite the aforementioned article when using this dataset.

Abstract

The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.

ZIP Folder Content

The ZIP folder comprises two main components: Captures and Datasets. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: BLE, IP-Based Packet, and IP-Based Flows.

To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.

This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.

Datasets' Content

Within this dataset, three sub-datasets are available, namely BLE, IP-Based Packet, and IP-Based Flows. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.

Identified Key Features Within Bluetooth Dataset

Feature Meaning

btle.advertising_header BLE Advertising Packet Header

btle.advertising_header.ch_sel BLE Advertising Channel Selection Algorithm

btle.advertising_header.length BLE Advertising Length

btle.advertising_header.pdu_type BLE Advertising PDU Type

btle.advertising_header.randomized_rx BLE Advertising Rx Address

btle.advertising_header.randomized_tx BLE Advertising Tx Address

btle.advertising_header.rfu.1 Reserved For Future 1

btle.advertising_header.rfu.2 Reserved For Future 2

btle.advertising_header.rfu.3 Reserved For Future 3

btle.advertising_header.rfu.4 Reserved For Future 4

btle.control.instant Instant Value Within a BLE Control Packet

btle.crc.incorrect Incorrect CRC

btle.extended_advertising Advertiser Data Information

btle.extended_advertising.did Advertiser Data Identifier

btle.extended_advertising.sid Advertiser Set Identifier

btle.length BLE Length

frame.cap_len Frame Length Stored Into the Capture File

frame.interface_id Interface ID

frame.len Frame Length Wire

nordic_ble.board_id Board ID

nordic_ble.channel Channel Index

nordic_ble.crcok Indicates if CRC is Correct

nordic_ble.flags Flags

nordic_ble.packet_counter Packet Counter

nordic_ble.packet_time Packet time (start to end)

nordic_ble.phy PHY

nordic_ble.protover Protocol Version

Identified Key Features Within IP-Based Packets Dataset

Feature Meaning

http.content_length Length of content in an HTTP response

http.request HTTP request being made

http.response.code Sequential number of an HTTP response

http.response_number Sequential number of an HTTP response

http.time Time taken for an HTTP transaction

tcp.analysis.initial_rtt Initial round-trip time for TCP connection

tcp.connection.fin TCP connection termination with a FIN flag

tcp.connection.syn TCP connection initiation with SYN flag

tcp.connection.synack TCP connection establishment with SYN-ACK flags

tcp.flags.cwr Congestion Window Reduced flag in TCP

tcp.flags.ecn Explicit Congestion Notification flag in TCP

tcp.flags.fin FIN flag in TCP

tcp.flags.ns Nonce Sum flag in TCP

tcp.flags.res Reserved flags in TCP

tcp.flags.syn SYN flag in TCP

tcp.flags.urg Urgent flag in TCP

tcp.urgent_pointer Pointer to urgent data in TCP

ip.frag_offset Fragment offset in IP packets

eth.dst.ig Ethernet destination is in the internal network group

eth.src.ig Ethernet source is in the internal network group

eth.src.lg Ethernet source is in the local network group

eth.src_not_group Ethernet source is not in any network group

arp.isannouncement Indicates if an ARP message is an announcement

Identified Key Features Within IP-Based Flows Dataset

Feature Meaning

proto Transport layer protocol of the connection

service Identification of an application protocol

orig_bytes Originator payload bytes

resp_bytes Responder payload bytes

history Connection state history

orig_pkts Originator sent packets

resp_pkts Responder sent packets

flow_duration Length of the flow in seconds

fwd_pkts_tot Forward packets total

bwd_pkts_tot Backward packets total

fwd_data_pkts_tot Forward data packets total

bwd_data_pkts_tot Backward data packets total

fwd_pkts_per_sec Forward packets per second

bwd_pkts_per_sec Backward packets per second

flow_pkts_per_sec Flow packets per second

fwd_header_size Forward header bytes

bwd_header_size Backward header bytes

fwd_pkts_payload Forward payload bytes

bwd_pkts_payload Backward payload bytes

flow_pkts_payload Flow payload bytes

fwd_iat Forward inter-arrival time

bwd_iat Backward inter-arrival time

flow_iat Flow inter-arrival time

active Flow active duration

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Server San Market Size 2024-2028

The server san market size is forecast to increase by USD 115.2 billion, at a CAGR of 36.51% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing adoption of e-commerce platforms and the convergence of server SAN solutions with cloud services. E-commerce websites require robust and efficient data storage solutions to handle large volumes of customer data and transactions. Server SAN technology offers the necessary performance, scalability, and flexibility to meet these demands. Additionally, cloud services are increasingly integrating server SAN solutions to enhance their offerings and provide customers with more options for data storage and management. However, the market faces challenges, primarily in the form of cybersecurity threats. With the increasing digitization of business operations and the growing amount of sensitive data being stored on server SANs, the risk of cyber attacks is heightened. Hackers are constantly seeking vulnerabilities to exploit, and data breaches can result in significant financial and reputational damage. Companies must invest in robust cybersecurity measures to protect their server SAN infrastructure and mitigate these risks. The ability to address these challenges effectively will be crucial for market success.

What will be the Size of the Server San Market during the forecast period?

Request Free SampleThe market continues to evolve, with dynamic market activities unfolding across various sectors. Asset protection remains a top priority, as energy efficiency gains increasing importance in data centers. Server rack cleaning and cable management are essential for operational efficiency and business continuity. Environmental sustainability is a growing concern, with server room sanitation and electrostatic disinfection integral to maintaining air quality and preventing bioaerosol control. Critical environments demand stringent cleanroom protocols and particle control to ensure data center hygiene and data security. Contamination prevention through microbial sampling and HVAC filtration is crucial for IT service continuity and emergency response. Preventive and corrective maintenance schedules are essential for risk mitigation and electrical safety, while adhering to industry standards and compliance regulations. Performance optimization and quality assurance are key objectives, with green data centers and sustainable practices becoming increasingly important. Water conservation and hydrogen peroxide vaporization are among the innovative solutions for IT infrastructure cleaning. Fire suppression and disaster recovery are essential for business continuity, with employee training and best practices ensuring effective implementation of safety procedures. Continuous improvement through performance optimization, antimicrobial coatings, and cost reduction are integral to the market's ongoing evolution. The market's dynamic nature underscores the importance of staying informed and adaptive to emerging trends and regulations.

How is this Server San Industry segmented?

The server san industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeHyperscaleEnterpriseEnd-userLargeSMEsDeployment TypeOn-PremisesCloud-BasedStageFibre ChanneliSCSINVMe-oFFC-NVMeGeographyNorth AmericaUSCanadaEuropeFranceGermanySpainUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanRest of World (ROW)

By Type Insights

The hyperscale segment is estimated to witness significant growth during the forecast period.Hyperscale server Storage Area Network (SAN) solutions have become essential for businesses managing large volumes of data and workloads. These organizations, including cloud providers, social media platforms, e-commerce giants, and other data-intensive enterprises, require storage systems that can effectively scale and perform optimally. Hyperscale server SANs offer the ability to expand storage capacity and performance in response to increasing demand, with a focus on horizontal scaling. Multiple storage nodes are added to the infrastructure to accommodate growing data and workload requirements. Energy efficiency and environmental sustainability are crucial considerations in the design of these solutions. Hyperscale server SANs incorporate advanced power management features and utilize renewable energy sources where possible. Cable management and server room sanitation are essential for maintaining operational efficiency and ensuring business continuity. Environmental monitoring and server downtime reduction are vital aspects of hyperscale server SANs. Real-time monitoring of temperature, humidity, and air quality ensures optimal server performance and reduces the risk of equipment

This data set is supplementary to the BSSA research article of Blanke et al. (2019), in which the local S-wave coda quality factor at The Geysers geothermal field, California, is investigated. Over 700 induced microseismic events recorded between June 2009 and March 2015 at 31 short-period stations of the Berkeley-Geysers Seismic Network were used to estimate the frequency-dependent coda quality factor (Q_C) using the method of Phillips (1985). A sensitivity analysis was performed to different input parameters (magnitude range, lapse time, moving window width, total coda length and seismic sensor component) to gain a better overview on how these parameters influence Q_C estimates. Tested parameters mainly show a low impact on the outcome whereas applied quality criteria like signal-to-noise ratio and allowed uncertainties of Q_C estimates were found to be the most sensitive factors. Frequency-dependent mean-Q_C curves were calculated from seismograms of induced earthquakes for each station located at The Geysers using the tested favored input parameters. The final results were tested in the context of spatio-temporal behavior of Q_C in the reservoir considering distance-, azimuth and geothermal production rate variations. A distance and azimuthal dependence was found which is related to the reservoir anisotropy, lithological-, and structural features. By contrast, variations in geothermal production rates do not influence the estimates. In addition, the final results were compared with previous estimated frequency-independent intrinsic direct S-wave quality factors (Q_D) of Kwiatek et al. (2015). A match of Q_D was observed with Q_C estimates obtained at 7 Hz center-frequency, suggesting that Q_D might not be of an intrinsic but of scattering origin at The Geysers. Additionally, Q_C estimates feature lower spreading of values and thus a higher stability. The Geysers geothermal field is located approximately 110 km northwest of San Francisco, California in the Mayacamas Mountains. It is the largest steam-dominated geothermal reservoir operating since the 1960s. The local seismicity is clearly related to the water injections and steam production with magnitudes up to ~5 occurring down to 5 km depth, reaching the high temperature zone (up to 360°C). The whole study area is underlain by a felsite (granitic intrusion) that shows an elevation towards the southeast and subsides towards northwest. A fracture network induces anisotropy into the otherwise isotropic rocks featuring different orientations. Moreover, shear-wave splitting and high attenuating seismic signals are observed and motivate to analyze the frequency-dependent coda quality factor. Two data sets were analyzed: one distinct cluster located in the northwest (NW) close to injection wells Prati-9 and Prati-29, and the other one southeast (SE) of The Geysers, California, USA, close to station TCH (38° 50′ 08.2″ N, 122° 49′ 33.7″ W and 38° 46′ 59.5″ N, 122° 44′ 13.2″ W, respectively). The frequency-dependent coda quality factor is estimated from the seismic S-wave coda by applying the moving window method and regression analysis of Phillips (1985). Different input parameters including moving widow width, lapse time and total coda length are used to obtain Q_C estimates and associated uncertainties. Within a sensitivity analysis we investigated the influence of these parameters and also of magnitude ranges and seismic sensor components on Q_C estimates. The coda analysis was performed for each event at each sensor component of each station. The seismograms were filtered in predefined octave-width frequency bands with center-frequencies ranging from 1-69 Hz. The moving window method was applied starting in the early coda (after the S-onset) for each frequency band measuring the decay of Power Spectral Density spectra. The decay of coda amplitudes was fitted with a regression line and Q_C estimates were calculated from its decay slope for each frequency band. In a final step a mean-Q_C curve was calculated for each available station within the study area resulting in different curves dependent on event location sites in the northwest and southeast. Data Description The data contain final mean-Q_C estimates of the NW and SE Geysers, coda Q estimates at 7 Hz center-frequency calculated by using the NW cluster, and initial direct Q estimates of Kwiatek et al. (2015) using the same data of the NW cluster. Table S1 shows final mean coda quality factor estimates obtained from the NW cluster at injection wells Prati-9 and Prati-29. The column headers show stations (station), center-frequencies of octave-width frequency bands in Hertz (f[Hz]), mean coda Q estimates (meanQc) and related standard deviations (std), all obtained by coda analysis. Table S2 shows the final mean coda quality factor estimates obtained from additional selected 100 events in the SE Geysers. Column headers correspond to those in Table S1. Table S3 shows coda Q estimates related to 7 Hz center-frequency. The column headers show stations (station), center-frequency of octave-width frequency bands in Hertz (f[Hz]), coda Q estimates at 7 Hz center-frequency (Q_C) and related standard deviations (std2sigma; 95% confidence level), all obtained by coda analysis. Table S4 shows selected direct S-wave quality factors of Kwiatek et al. (2015) obtained by spectral fitting. The column headers show stations (station) and direct S-wave Q estimates (Q_D). The four tables are provided in tab separated txt format. Tables S3 and S4 are used for a comparative study and displayed in Figure 12 of the BSSA article mentioned above.

Maryland Bicycle Level of Traffic Stress (LTS) An overview of the methodology and attribute data is provided below. For a detailed full report of the methodology, please view the PDF published by the Maryland Department of Transportation here. The Maryland Department of Transportation is transitioning from using the Bicycle Level of Comfort (BLOC) to using the Level of Traffic Stress (LTS) for measuring the “bikeability” of the roadway network. This transition is in coordination with the implementation of MDOT SHA’s Context Driven Design Guidelines and other national and departmental initiatives. LTS is preferred over BLOC as LTS requires fewer variables to calculate including:Presence and type of bicycle facilitySpeed limitNumber of Through Lanes/Traffic VolumeTraditionally, the Level of Traffic Stress (LTS) (scale “1” to “4”) is a measure for assessing the quality of the roadway network for its comfort with various bicycle users. The lower the LTS score, the more inviting the bicycle facility is for more audiences.LTS Methodology (Overview) MDOT’s LTS methodology is based on the metrics established by the Mineta Transportation Institute (MTI) Report 11-19 “Low-Stress Bicycling and Network Connectivity (May 2012) - additional criteria refined by Dr. Peter G. Furth (June 2017) below and Montgomery County's Revised Level of Traffic Stress. Shared-use Path Data Development and Complimentary Road Separated Bike Routes DatasetA complimentary dataset – Road Separated Bike Routes, was completed prior to this roadway dataset. It has been provided to the public via (https://maryland.maps.arcgis.com/home/item.html?id=1e12f2996e76447aba89099f41b14359). This first dataset is an inventory of all shared-use paths open to public, two-way bicycle access which contribute to the bicycle transportation network. Shared-use paths and sidepaths were assigned an LTS score of “0” to indicate minimal interaction with motor vehicle traffic. Many paved loop trails entirely within parks, which had no connection to the adjacent roadway network, were not included but may be included in future iterations. Sidepaths, where a shared-use path runs parallel to an adjacent roadway, are included in this complimentary Road Separated Bike Routes Dataset. Sidepaths do not have as an inviting biking environment as shared-use paths with an independent alignment due to the proximity of motor vehicle traffic in addition to greater likelihood of intersections with more roadways and driveways. Future iterations of the LTS will assign an LTS score of “1” to sidepaths. On-street Bicycle Facility Data Development This second dataset includes all on-road bicycle facilities which have a designated roadway space for bicycle travel including bike lanes and protected bike lanes. Marked shared lanes in which bicycle and motor vehicle traffic share travel lanes were not included. Shared lanes, whether sharrows, bike boulevards or signed routes were inventoried but treated as mixed traffic for LTS analysis. The bicycle facilities included in the analysis include:

Standard Bike Lanes – A roadway lane designated for bicycle travel at least 5-feet-wide. Bike lanes may be located against the curb or between a parking lane and a motor vehicle travel lane. Buffered bike lanes without vertical separation from motor vehicle traffic are included in this category. Following AASHTO and MDOT SHA design standards, bike lanes are assumed to be at least 5-feet-wide even through some existing bike lanes are less than 5-feet-wide.
Protected Bike Lanes – lanes located within the street but are separated from motor vehicle travel lanes by a vertical buffer, whether by a row of parked cars, flex posts or concrete planters. Shoulders – Roadway shoulders are commonly used by bicycle traffic. As such, roadways with shoulders open to bicycle traffic were identified and rated for LTS in relation to adjacent traffic speeds and volumes as well as the shoulder width. Shoulders less than 5-feet-wide, the standard bike lane width, were excluded from analysis and these roadway segments were treated as mixed traffic.

The Office of Highway Development at MDOT SHA provided the on-street bicycle facility inventory data for state roadways. The shared-use path inventory and on-street bicycle facility inventory was compiled from local jurisdiction’s open-source download or shared form the GIS/IT departments. Before integrating into OMOC, these datasets were verified by conducting desktop surveys and site visits, and by consulting with local officials and residents.
Data UsesThe 2022 LTS data produced through this process can be used in a variety of planning exercises. The consistent metrics applied across the state will help inform bicycle mobility and accessibility decisions at state and local levels. Primarily, the LTS analysis illustrates how bikeable Maryland roads are where the greatest barriers lie. While most roads in the state are an LTS 1, the main roadways which link residential areas with community services are typically LTS 4. In the coming months, MDOT will use the LTS in variety of way including:

Conducting a bicycle network analysis to develop accessibility measures and potential performance metrics. Cross-referencing with state crash location data; Performing gap analysis to help inform project prioritization.

Data Limitations A principle of data governance MDOT strives to provide the best possible data products. While the initial LTS analysis of Maryland’s bicycle network has many uses, it should be used with a clear understanding of the current limitations the data presents.

Assumptions - As noted earlier in this document, some of the metrics used to determine LTS score were estimated. Speed limits for many local roadways were not included in the original data and were assigned based on the functional classification of the roadway. Speed limits are also based on the posted speed limit, not the prevailing operating vehicle speeds which can vary greatly. Such discrepancies between actual and assumed conditions could introduce margins of error in some cases. As data quality improves with future iterations, the LTS scoring accuracy will also improve. Generalizations - MDOT’s LTS methodology follows industry standards but needs to account for varying roadway conditions and data reliability from various sources. The LTS methodology aims to accurately capture Maryland’s bicycle conditions and infrastructure but must consider data maintenance requirements. To limit data maintenance generalizations were made in the methodology so that a score could be assigned. Specifically, factors such as intersections, intersection approaches and bike lane blockages are not included in this initial analysis. LTS scores may be adjusted in the future based on MDOT review, updated industry standards, and additional LTS metrics being included in OMOC such as parking and buffer widths.
Timestamped - As the LTS score is derived from a dynamic linear referencing system (LRS), any LTS analysis performed reflects the data available in OMOC. Each analysis must be considered ‘timestamped’ and becoming less reliable with age. As variables within OMOC change, whether through documented roadway construction, bikeway improvements or a speed limit reduction, LTS scores will also change. Fortunately, as this data is updated in the linear referencing system, the data becomes more reliable and LTS scores become more accurate. --------------------------------------------------------------------------------------------------------------------------------------------------------------------Level of Traffic Stress (LTS) Attribute Metadata OBJECTID | GIS Object IDState ID (ID) | Unique identification number provided by Maryland State Highway Administration (MDOT SHA)Route ID (ROUTEID) | Unique identification number for the roadway segment/record as determined by Maryland State Highway Administration (MDOT SHA) From Measure (FROMMEASURE) | The mileage along the roadway record that the specific roadway conditions change and maintain the same conditions until To MeasureTo Measure (TOMEASURE) | The mileage along the roadway record that the specific roadway conditions change and maintain the same conditions since From MeasureRoadway Functional Class (FUNCTIONAL_CLASS) | The functional classification of the roadway as determined by the Federal Highway Administration in coordination with the Maryland Department of Transportation State Highway Administration (MDOT SHA). All roadway records have a functional classification value. The following values represent the functional classification:

1 - Local 2 - Minor collector 3 - Major collector 4 - Minor arterial 5 - Principal Arterial (other) 6 - Principal Arterial (other Freeways and Expressways) 7 - Interstate

Annual Average Daily Traffic (AADT) | The Annual Average Daily Traffic (AADT) represents the average number of motor vehicles that pass along a roadway segment during a 24-hour period. The value is derived from MDOT SHA’s Traffic Monitoring System (TMS), the state’s clearinghouse for all traffic volume records. Roadway Speed Limit (SPEED_LIMIT) | The posted speed limit for a roadway segment as assigned by the MDOT SHA for state roadways and the local jurisdiction’s transportation management agency. Values for SPEED_LIMIT are measured in miles per hour (mph) in 5 mph increments from 5 mph through 70 mph. Roadway Access Control (ACCESS_CONTROL) | The access control indicates the types of entry points along the roadway segment, ranging from full to no access control. Interstates and other state roadways with no at-grade crossings are full access control, whereas a neighborhood street open to all modes of traffic represents a roadway with no access control. The values in

The potential for automated vehicles (AVs) to reduce parking to allow for the conversion of on-and off-street parking to new uses, such as new space for walk, bike, and shared -micro-mobility services, and housing), has sparked significant interest among urban planners. AVs could drop-off and pick-up passengers in areas where parking costs are high or limited. Personal AVs could return home or park in less expensive locations and shared AVs could serve other passengers. However, reduced demand for parking would be accompanied by increased demand for curbside drop-off/pick-up space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flow in downtown urban areas during peak hours when high volumes of drop-offs and pick-ups events are likely to occur. Only limited research examines the travel and greenhouse gas effects (GHG) of a shift from parking to drop-off/pick-up travel and the effects of changes in parking supply. Our study uses a microscopic road traffic model with local travel activity data to simulate vehicle travel in San Francisco’s downtown central business district to explore traffic flow, VMT, and GHG effects of AV scenarios in which we vary (1) the demand for drop-off and pick-up travel versus parking, (2) the supply of on-street and off-street parking, and (3) the change in demand for parking and drop-off/pick-up travel due to a significant change in price of using curbside space.

Methods Demand Modeling

We selected the microscopic road traffic model (Simulation of Urban MObility or SUMO) to simulate the traffic flow effects of the AV scenarios. SUMO is an open-source, highly portable, multimodal, microscopic road traffic simulation package designed to handle large road networks (Behrisch et al., 2011). The SUMO simulation used local travel activity data from the San Francisco Bay Area MATsim (SFBA-MATsim) model (Horni et al., 2016; Jaller et al., 2019; Rodier et al., 2018). This model was developed and calibrated with the official San Francisco Bay Area Metropolitan Transportation Commission’s Activity-Based Travel Demand Model (MTC-ABM).

The geographic focus of this study is the central business district (CBD) in the City of San Francisco. We selected individual daily activity tours with at least one vehicle stop in st CBD during the 24 hours (an average weekday) from the SFBA-MATsim model. Arrival and departure times for vehicle tour stops are in increments of seconds in the SFBA-MATsim model. We also converted transit vehicle stops to AVs stops for purposes of the scenario simulation. From the SFBA-MATsim model, we obtained about 900,000 travelers making 1.8 million trips with about 1% of the trips representing internal trips and the remainder had at least one stop in the study area. Total simulated vehicle trip volumes in the network were adjusted to match roadway supply (see discussion below), transit supply, and model year congestion levels.

Network and Traffic Analysis Zones

The SUMO simulations use transportation analysis zones (TAZ) that are consistent with both MTC-ABM and SFBA-MATsim models’ zone system. The TAZs in the study area are among the smallest in the region and include small numbers of census blocks. For this specific network, there are 45 TAZs in total.

We used the SUMO network editor to import the OpenStreetMap for the San Francisco CBD roadway network. We edited the OpenStreetMap roadway network to exclude minor roads. Major roads in the CBD were included in the final network to increase the efficiency of SUMO simulations.

Parking Supply Data

The San Francisco (SF) Parking Census is the source of the parking supply data used in this study1. The San Francisco Municipal Transportation Agency (SFMTA) collected the parking supply data: 97% through field surveys and 3% through remote resources. The on-street parking supply in the dataset includes metered on-street spaces, non-metered demarcated spaces (parking stalls), and non-metered un-demarcated spaces (unmarked curb length). For non-metered spots, we apply a standard 17 feet per parking space, which is the length needed by an average sedan to park between two vehicles. If there was a short length of curb space that could only support one vehicle, we used 12 feet as the length of the parking spot. For any unmarked perpendicular parking, we used a standard of eight feet and six inches of curb space. We did not include controlled parking and restricted parking spaces in the data set.

We used ArcGIS to model the parking data and transferred the data to SUMO using spatial analysis tools. The data set included 1,351 on-street parking locations with a total capacity of 20,019 parking spots within the study area. For off-street parking, there are 356 locations with a total capacity of 65,404 parking spots.

1 http://sfpark.org/resources-overview/

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Home Wi-Fi Router Market Size 2025-2029

The home wi-fi router market size is forecast to increase by USD 3.34 billion, at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for rise network infrastructure to support the rise in remote work, online learning, and streaming services. A key trend In the market is the formation of partnerships between telecom network providers and Wi-Fi router manufacturers, enabling seamless integration and improved performance. These collaborations are enhancing network coverage, reliability, and security, making them a popular choice among consumers. The need for high-speed and secure Wi-Fi connections is becoming increasingly essential, particularly in North America, where the adoption of smart home devices and the Internet of Technology is on the rise. Additionally, the ongoing advancements in Wi-Fi technology, such as Wi-Fi 6 and 6E, are further fueling market growth by offering faster speeds, better coverage, and lower latency. Overall, the market is expected to continue its growth trajectory, driven by these trends and the increasing demand for reliable and secure home networking solutions.

What will be the size of the Home Wi-Fi Router Market During the Forecast Period?

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The market is a dynamic and evolving segment of the information technology and telecommunications industry, catering to the growing demand for high-speed internet access for private computers, smartphones, laptops, and smart home devices. Traditional wired Local Area Networks (LAN) have given way to Wireless LANs (WLANs), with dual-band wireless routers becoming increasingly popular due to their ability to support multiple devices and minimize interference.
Technology advancements, such as hybrid wired-wireless routers, PoE (Power over Ethernet) ports, and FXS ports, have expanded the functionality of Wi-Fi routers, making them essential components of modern home networks. The market scenario is influenced by the latest trends, including the integration of Virtual Private Networks (VPNs) and the emergence of smart home technology. The market is expected to continue its growth trajectory, driven by the increasing reliance on technology for work, education, and entertainment.

How is this Home Wi-Fi Router Industry segmented and which is the largest segment?

The home wi-fi router industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Device

  Fixed Wi-Fi router
  Mobile Wi-Fi router


Distribution Channel

  Offline
  Online


Type

  Dual-band router
  Tri-band router
  Mesh router
  Single-band router


Geography

  North America

    Canada
    US


  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France


  South America

    Brazil


  Middle East and Africa

By Device Insights

The fixed Wi-Fi router segment is estimated to witness significant growth during the forecast period.

Mobile Wi-Fi routers serve as convenient solutions for accessing high-speed internet without the need for a wired local area network. These devices connect users to the internet via telecom networks and can be battery-operated or plugged in. Ideal for small areas, such as a single room, mobile Wi-Fi routers enable multiple users to connect their smartphones, laptops, tablets, and other smart devices. Internet speed is a significant factor, as it may decrease with an increasing number of connected devices. The expansion of internet penetration and advancements in telecom infrastructure, including 4G and 5G technologies, cost-effective mobile data packs, and affordable monthly subscription plans, are driving the adoption of mobile Wi-Fi routers.

Further, security concerns and interoperability with global standards are crucial considerations for companies. Single-band, dual-band, and triband wireless routers operate on various frequency bands, catering to the demands of end consumers in diverse industries, such as healthcare, business, and IoT devices. Wi-Fi technology continues to evolve, with AI-powered routers, smart homes, and VPNs, such as ExpressVPN and Aircove, offering advanced features. company analysis, market research reports, and accurate market data from sources like the ITU, AT&T, and GSMA Intelligence provide insights into the latest trends and market scenarios.

Get a glance at the Home Wi-Fi Router Industry report of share of various segments Request Free Sample

The fixed Wi-Fi router segment was valued at USD 2.04 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 40% to the growth of the global market during the foreca

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Big Data And Analytics Market In Telecom Industry Size 2025-2029

The big data and analytics market in telecom industry size is forecast to increase by USD 9.03 billion, at a CAGR of 14.7% between 2024 and 2029.

The Big Data and Analytics market in the Telecom industry is experiencing significant growth, driven primarily by the surge in data volumes generated by an increasing number of connected devices and the adoption of 5G technology. Telecom companies are capitalizing on this trend by introducing new data analytics solutions to gain insights from the vast amounts of data they collect. However, this growth comes with challenges. Data privacy and regulatory compliance are becoming increasingly important, with stricter regulations being implemented to protect customer data. Telecom companies must invest in robust data security measures and ensure they are in compliance with these regulations to maintain customer trust and avoid costly fines. Additionally, the complexity of managing and analyzing large data sets can be a challenge, requiring significant IT resources and expertise. To remain competitive, telecom companies must effectively navigate these challenges and continue to innovate in the realm of data analytics to provide value-added services to their customers.

What will be the Size of the Big Data And Analytics Market In Telecom Industry during the forecast period?

Request Free SampleIn the telecom industry, big data and analytics continue to play a pivotal role in driving innovation and enhancing network performance. The application of advanced technologies such as cloud computing, artificial intelligence, network forensics, and sentiment analysis, among others, is transforming the way telecom infrastructure is managed and optimized. Network dynamics are constantly evolving, with new challenges and opportunities arising in areas like network availability, data transformation, customer relationship management, and network security. Telecom companies are leveraging data integration, network modeling, and data cleansing to gain insights into network behavior and customer preferences. Satellite communications, wireless networks, and fiber optic networks are being optimized using network optimization algorithms and predictive analytics to improve network reliability and performance. Telecom network optimization is also a key focus area, with 5G network analytics and network virtualization gaining traction. Data privacy, fraud detection, and compliance regulations are critical concerns for telecom companies, and data security is a top priority. Machine learning algorithms and network security analytics are being used to enhance network intrusion detection and prevent data breaches. Customer segmentation and targeted marketing are other areas where big data and analytics are making a significant impact. Real-time analytics and data visualization tools are enabling telecom companies to gain actionable insights and make data-driven decisions. Telecom infrastructure is being transformed through big data and analytics, with network management systems and network orchestration playing a crucial role in ensuring seamless integration and optimization of various network components. The ongoing unfolding of market activities and evolving patterns in the telecom industry underscore the importance of staying abreast of the latest trends and technologies.

How is this Big Data And Analytics In Telecom Industry Industry segmented?

The big data and analytics in telecom industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentHardwareServicesSoftwareApplicationNetwork optimizationCEEFD and POperational efficiencyRevenue assuranceAnalytics TypeCustomer AnalyticsNetwork AnalyticsMarketing AnalyticsDeployment ModelCloud-BasedOn-PremisesGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.In the telecom industry, the integration of cloud computing and artificial intelligence (AI) is revolutionizing big data and analytics. Telecom companies leverage AI for network forensics, sentiment analysis, fraud detection, customer churn prediction, and network optimization. Network modeling utilizes satellite communications and wireless networks to analyze customer behavior and optimize network performance. Data integration is crucial for merging data from various sources, ensuring data transformation and data quality assurance. 5G network analytics necessitates robust data processing capabilities. Telecom companies invest in big data infrastructure, including network optimization algorithms, data

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Satellite-enabled IoT Market Size 2024-2028

The satellite-enabled iot market size is forecast to increase by USD 2.31 billion at a CAGR of 21.92% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing usage of the Internet of Things (IoT) in various industries and the substantial research and development (R&D) spending in the aerospace and defense sectors. The integration of IoT with satellite technology enables real-time data collection and transmission from remote locations, making it an essential solution for various applications, including agriculture, transportation, and energy. However, the market is not without challenges. Regulatory issues surrounding satellite operations and services pose a significant hurdle, requiring companies to comply with stringent regulations and standards. Despite these challenges, the market's strategic landscape offers ample opportunities for companies to capitalize on the growing demand for satellite-enabled IoT solutions. Companies seeking to navigate this market effectively should focus on developing innovative technologies, complying with regulatory requirements, and collaborating with key industry players to expand their reach and offer comprehensive solutions.

What will be the Size of the Satellite-enabled IoT Market during the forecast period?

Request Free SampleThe market encompasses the use of Low Earth Orbit (LEO) satellites for direct-to-satellite connectivity, particularly in less developed countries and defense organizations. This market has gained traction due to the increasing demand for high-speed broadband in remote areas and the need for real-time situational awareness. LEO satellites offer advantages such as low latency, high-frequency bands like Ka-band and V-band, and the ability to cover vast geographical areas. The market's growth can be attributed to the proliferation of IoT devices, the adoption of AI-based algorithms, and the integration of satellite technology with various industries, including agriculture, nature monitoring, and economic activities. The 1990s saw the initial development of LEO satellite constellations, but recent advancements in technology have led to an opportunity for growth, with companies like Myriota, Astrocast, and Hiberband Direct entering the fray. Despite the potential, challenges remain, such as cybersecurity attacks and the need for ground gateways. As 5G networks continue to roll out, the integration of satellite technology with terrestrial networks may further expand the market's reach. Overall, the market is poised for significant growth, offering a promising future for various applications, from precision agriculture to industrialized countries' economic development.

How is this Satellite-enabled IoT Industry segmented?

The satellite-enabled iot industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. SourceLarge enterpriseSMEsServiceDirect-to-satelliteSatellite IoT backhaulGeographyNorth AmericaUSEuropeFranceRussiaAPACChinaJapanMiddle East and AfricaSouth America

By Source Insights

The large enterprise segment is estimated to witness significant growth during the forecast period.The Satellite IoT market encompasses LEO (Low Earth Orbit) satellites that enable economic activities in industrialized and less developed countries. LEO-based services, such as precision farming with GPS, navigation applications, nature monitoring, and disaster response, are gaining traction. High-speed broadband via Ka-band and direct-to-satellite connectivity offer real-time data transfer for IoT devices, especially in remote areas with limited terrestrial network coverage. Challenges in satellite IoT include cybersecurity attacks and the need for advanced AI-based algorithms for data processing. New farming techniques like Precision Agriculture 4.0 and the gas industry are major consumers of satellite IoT services. Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites also play a role, but LEO satellites' lower altitude offers advantages for IoT applications. Satellite IoT market dynamics include increasing demand for satellite IoT backhaul for real-time data transfer, the growing number of IoT connections, and the need for application servers and central control systems. V-band spectrum is an emerging technology for satellite IoT. Market players like Astrocast and Myriota are focusing on providing low-cost, high-data-rate satellite IoT solutions. Satellite IoT applications include positioning, asset management, situational awareness, and disaster response. IoT devices require direct-to-satellite services for connectivity, bypassing the need for ground gateways. The satellite IoT market presents opportunities for various industries