The dataset includes total incoming calls, both emergency (9-1-1) and non-emergency, and outgoing non-emergency. Emergency calls are broken down by type of call, whether from a cell phone, landline, etc.

Monthly volume of E911 calls received by Cook County ETSB 911. Counts are broken up by call type and Remote Site location. The Cook County ETSB provides 9-1-1 services for all Unincorporated Cook County and the municipalities of Dixmoor, Ford Heights, Golf, Northlake, Phoenix, Robbins, and Stone Park. Incoming Call Volume by Type: Statistics gathered by the caller class of service and by caller trunk number. Remote Site Transfer Call Volume: Transferred Call Volume of each Cook County ETSB remote site for the month.

All incident segments for each of the first responding agencies (PD, FD and EMS) that contribute to the end-to-end response times. This data set provides call volumes broken down by incident type for each Week Start time period as well as the timestamps and average response times (in seconds) for each segment of the call. For the Incident Type Definitions please refer to this link.

This dataset outlines the emergency communications call volume by month and hour.

Analysis of ‘ETSB 911 Monthly Call Volume’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7ff7bb52-7f7f-45b7-90e0-0f92ce1fe0cb on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Monthly volume of E911 calls received by Cook County ETSB 911. Counts are broken up by call type and Remote Site location. The Cook County ETSB provides 9-1-1 services for all Unincorporated Cook County and the municipalities of Dixmoor, Ford Heights, Golf, Northlake, Phoenix, Robbins, and Stone Park.

Incoming Call Volume by Type: Statistics gathered by the caller class of service and by caller trunk number. Remote Site Transfer Call Volume: Transferred Call Volume of each Cook County ETSB remote site for the month.

--- Original source retains full ownership of the source dataset ---

Spreadsheet for 911 statistics _DEMO DATA

This dataset shows all calls processed by emergency communications personnel in Norfolk, VA. The Emergency Communications Center is where calls are received from the public and then dispatched to public safety personnel (police, fire-rescue). The dataset shows daily (24-hour period of operations) calls and texts processed by emergency communications personnel. This dataset will be updated daily.

Please note: Data from April 5, 2023 to May 23, 2023 is not available due to equipment upgrades during this timeframe.

Calls for Service to NYPD's 911 system
This dataset documents entries into the NYPD 911 system, ICAD. The data is collected from the ICAD system which call takers and dispatchers use to communicate with callers and the NYPD. Each record represents an entry into the system. The data includes entries generated by members of the public as well as self-initiated entries by NYPD Members of Service. The data can be used for issues being responded to by the NYPD.

Emergency medical services (EMS) systems increasingly grapple with rising call volumes and workforce shortages, forcing systems to decide which responses may be delayed. Limited research has linked dispatch codes, on-scene findings, and emergency department (ED) outcomes. This study evaluated the association between dispatch categorizations and time-critical EMS responses defined by prehospital interventions and ED outcomes. Secondarily, we proposed a framework for identifying dispatch categorizations that are safe or unsafe to hold in queue. This retrospective, multi-center analysis encompassed all 9-1-1 responses from 8 accredited EMS systems between 1/1/2021 and 06/30/2023, utilizing the Medical Priority Dispatch System (MPDS). Independent variables included MPDS Protocol numbers and Determinant levels. EMS treatments and ED diagnoses/dispositions were categorized as time-critical using a multi-round consensus survey. The primary outcome was the proportion of EMS responses categorized as time-critical. A non-parametric test for trend was used to assess the proportion of time-critical responses Determinant levels. Based on group consensus, Protocol/Determinant level combinations with at least 120 responses (∼1 per week) were further categorized as safe to hold in queue (10% time-critical ED outcome). Of 1,715,612 EMS incidents, 6% (109,250) involved a time-critical EMS intervention. Among EMS transports with linked outcome data (543,883), 12% had time-critical ED outcomes. The proportion of time-critical EMS interventions increased with Determinant level (OMEGA: 1%, ECHO: 38%, p-trend < 0.01) as did time-critical ED outcomes (OMEGA: 3%, ECHO: 31%, p-trend < 0.01). Of 162 unique Protocols/Determinants with at least 120 uses, 30 met criteria for safe to hold in queue, accounting for 8% (142,067) of incidents. Meanwhile, 72 Protocols/Determinants met criteria for unsafe to hold, accounting for 52% (883,683) of incidents. Seven of 32 ALPHA level Protocols and 3/17 OMEGA level Protocols met the proposed criteria for unsafe to hold in queue. In general, Determinant levels aligned with time-critical responses; however, a notable minority of lower acuity Determinant level Protocols met criteria for unsafe to hold. This suggests a more nuanced approach to dispatch prioritization, considering both Protocol and Determinant level factors.

AI-Enhanced Emergency Call Text Analysis Market Outlook

As per our latest research, the global AI-Enhanced Emergency Call Text Analysis market size reached USD 1.28 billion in 2024, driven by the increasing adoption of artificial intelligence in public safety and emergency response systems. The market is projected to grow at a robust CAGR of 18.3% from 2025 to 2033, reaching a forecasted value of USD 5.34 billion by 2033. This impressive growth is primarily fueled by advancements in natural language processing (NLP), the rising demand for real-time incident analysis, and the integration of AI-powered solutions in emergency communication networks globally.

A major growth factor for the AI-Enhanced Emergency Call Text Analysis market is the rapid evolution of AI and machine learning algorithms, particularly in the field of natural language processing. These advancements have enabled emergency response centers to analyze and interpret textual emergency call data with unprecedented accuracy and speed, significantly reducing response times and improving situational awareness. As emergency calls increasingly include SMS, chat, and social media inputs, the ability to automatically extract actionable information from large volumes of unstructured text has become essential. The integration of AI-driven sentiment analysis, keyword detection, and real-time translation further enhances the effectiveness of these systems, enabling first responders to prioritize incidents and allocate resources more efficiently. This technological progress is being embraced by governments and public safety agencies worldwide, who recognize the potential of AI to save lives and optimize emergency operations.

Another key driver is the growing complexity and volume of emergency communications, which has outpaced the capabilities of traditional manual dispatch systems. The proliferation of mobile devices and digital communication channels has led to a surge in text-based emergency calls, necessitating robust and scalable AI solutions that can handle high volumes of data in real time. AI-enhanced text analysis platforms are now being deployed to automatically classify emergencies, flag critical incidents, and provide decision support to dispatchers. These systems not only improve operational efficiency but also help mitigate risks associated with human error and information overload. The increasing emphasis on interoperability and integration with existing computer-aided dispatch (CAD) systems further amplifies the demand for advanced AI-powered text analysis tools, positioning the market for sustained long-term growth.

In addition, regulatory mandates and public safety initiatives are playing a significant role in accelerating market adoption. Many governments have introduced guidelines and funding programs to modernize emergency communication infrastructure, with a strong focus on leveraging AI and data analytics. The COVID-19 pandemic highlighted the importance of agile and intelligent emergency response systems, prompting renewed investment in AI-driven solutions for crisis management and disaster response. Collaborations between technology vendors, telecom operators, and public agencies are fostering innovation and facilitating the deployment of next-generation emergency call analysis platforms. As a result, the market is witnessing a surge in pilot projects and large-scale implementations, particularly in regions with advanced digital infrastructure.

From a regional perspective, North America currently dominates the AI-Enhanced Emergency Call Text Analysis market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of leading technology providers, well-established public safety networks, and proactive government initiatives has positioned North America at the forefront of market adoption. Europe is also experiencing significant growth, driven by cross-border emergency response collaborations and stringent regulatory frameworks. Meanwhile, the Asia Pacific region is expected to witness the fastest CAGR over the forecast period, fueled by rapid urbanization, increasing investments in smart city projects, and the rising incidence of natural disasters. Latin America and the Middle East & Africa are gradually catching up, supported by efforts to modernize emergency communication systems and improve public safety outcomes.

description: This data represents all emergency medical services calls related to possible opioid abuse. Opioid Abuse Probable A call may be coded as opioid abuse probable for many reasons, such as * Are there are any medical symptoms indicative of opioid abuse? * Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)? * Are there witnesses or patient statements made that point to opioid abuse? * Is there any other evidence that opioid abuse is probable with the patient? Opioid abuse probable is determined by Tempe Fire Medical Rescue Departments Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given Narcan/Naloxone Given refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses. Groups Groups are used to determine if there are specific populations that have an increase in opioid abuse. * The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well. * The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempes Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well. * The unknown group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. Gender Patient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine. Data Set History Data sets were evolving in 2017 due to software upgrades and identifying new parameters to focus data collection on. The 2018 data will be a more comprehensive set of data that includes all the fields identified throughout 2017. Data sets may continue to evolve based on the needs of the community and healthcare trends. Information about the data can be found at Data Documentation; abstract: This data represents all emergency medical services calls related to possible opioid abuse. Opioid Abuse Probable A call may be coded as opioid abuse probable for many reasons, such as * Are there are any medical symptoms indicative of opioid abuse? * Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)? * Are there witnesses or patient statements made that point to opioid abuse? * Is there any other evidence that opioid abuse is probable with the patient? Opioid abuse probable is determined by Tempe Fire Medical Rescue Departments Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given Narcan/Naloxone Given refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses. Groups Groups are used to determine if there are specific populations that have an increase in opioid abuse. * The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well. * The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempes Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well. * The unknown group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. Gender Patient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine. Data Set History Data sets were evolving in 2017 due to software upgrades and identifying new parameters to focus data collection on. The 2018 data will be a more comprehensive set of data that includes all the fields identified throughout 2017. Data sets may continue to evolve based on the needs of the community and healthcare trends. Information about the data can be found at Data Documentation

AI-Enhanced Emergency Call Text Analysis Market Outlook

As per our latest research findings, the AI-Enhanced Emergency Call Text Analysis market size reached USD 1.42 billion in 2024 globally. The market is projected to expand at a robust CAGR of 23.7% from 2025 to 2033, fueled by the rising adoption of artificial intelligence in public safety communications. By 2033, the market is anticipated to attain a value of USD 11.85 billion. The primary growth factor driving this market is the increasing reliance on AI-powered solutions for rapid, accurate, and context-aware analysis of emergency call texts, which significantly enhances the efficiency and effectiveness of emergency response systems worldwide.

The growth of the AI-Enhanced Emergency Call Text Analysis market is underpinned by the escalating volume of emergency calls and the growing complexity of incidents that require swift and precise intervention. As urbanization accelerates and populations increase, public safety agencies are experiencing a surge in both voice and text-based emergency communications. Traditional manual analysis methods are often inadequate for handling such large volumes of data in real time. The integration of AI-driven text analysis enables agencies to quickly extract critical information, identify patterns, and prioritize responses, thereby reducing response times and potentially saving more lives. This technological advancement is especially crucial in metropolitan areas where the density of emergency incidents is higher, making rapid and accurate triage indispensable.

Another major growth factor for the market is the rising demand for multilingual and context-sensitive analysis capabilities. Emergency call centers increasingly receive communications in multiple languages and dialects, posing significant challenges for human operators. AI-enhanced solutions can be trained on vast datasets in various languages, allowing for real-time translation and contextual analysis. This ensures that no critical information is lost due to language barriers, improving the inclusivity and effectiveness of emergency services. Furthermore, advancements in natural language processing (NLP) and sentiment analysis empower these systems to detect urgency, distress, and intent, even when explicit keywords are absent, leading to better prioritization and resource allocation.

The proliferation of mobile devices and the expansion of digital communication channels have also significantly contributed to the market’s growth. With the increasing use of SMS, chat apps, and social media for emergency reporting, public safety agencies are compelled to adopt AI-driven solutions that can seamlessly analyze and integrate textual data from diverse sources. The ability of AI-enhanced platforms to aggregate, cross-reference, and analyze data from multiple channels in real time not only improves situational awareness but also supports coordinated responses across different agencies. This holistic approach to emergency communication management is becoming a standard requirement, further propelling the adoption of AI-enhanced emergency call text analysis solutions.

Regionally, North America leads the market, driven by substantial investments in public safety infrastructure and early adoption of advanced technologies by government and law enforcement agencies. The region benefits from a mature ecosystem of AI vendors and a strong regulatory framework supporting technological innovation in emergency services. Europe follows closely, with significant growth observed in countries prioritizing smart city initiatives and cross-border emergency collaboration. The Asia Pacific region is witnessing the fastest CAGR, attributed to rapid urbanization, increasing government spending on public safety, and the rising adoption of AI-driven solutions in emerging economies. Meanwhile, Latin America and the Middle East & Africa are gradually embracing these technologies, with pilot projects and public-private partnerships paving the way for broader market penetration.

Component Analysis

The Component segment of the AI-Enhanced Emergency Call Text Analysis market is categorized into Software, Hardware, and Services. The software component commands the largest share, owing to the critical role of advanced algorithms, natural language processing engines, and machine lea

Fire Department Service Calls since 2019This dashboard is updated weekly

Data Description: Fire Incident data includes all fire incident responses. This includes emergency medical services (EMS) calls, fires, rescue incidents, and all other services handled by the Fire Department. All runs are coded according to classification: for EMS, this includes ALS (advanced life support); BLS (basic life support); etc.

Data Creation: This data is created when a run is entered into the City of Cincinnati’s computer-aided dispatch (CAD) database.

Data Created By: The source of this data is the City of Cincinnati's computer aided dispatch (CAD) database.

Refresh Frequency: This data is updated daily.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/6jrc-cmn5

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Since April 2014, Local Health Boards have been responsible for providing emergency ambulance services (999 calls) for their local residents; the Welsh Ambulance Services NHS Trust (WAST) is commissioned to deliver emergency ambulance services on their behalf. As announced in a statement by the Deputy Minister for Health, a new clinical response model was implemented in Wales from 1 October 2015. The new model has three new categories of calls – Red, Amber and Green: • Red - Immediately life-threatening (someone is in imminent danger of death, such as a cardiac arrest). There is a target for 65 per cent of these calls to have a response within 8 minutes. • Amber- Serious but not immediately life-threatening (patients who will often need treatment to be delivered on the scene, and may then need to be taken to hospital). There will be no time-based target for amber calls; instead a range of clinical outcome indicators will be introduced to measure the quality, safety and timeliness of care being delivered alongside patient experience information, which will be published every quarter. • Green - Non urgent (can often be managed by other health services) and clinical telephone assessment. There is no official time based target for these calls. Running calls (operational crews who arrive at the scene of an unrecorded incident without prior receipt of an emergency call) are counted as red calls, as are calls answered by either a Health Care Professional on Scene with a Defibrillator (MEDIC), or a Public Access Defibrillator (PAD). Health Care Professionals¹ (HCP) Urgent & Planned Calls are identified as green; where an HCP call poses an immediate threat to life, these calls will be prioritised according the final Medical Priority Dispatch System priority. ¹ Doctor, General Practitioner, Emergency Care Practitioner, Nurse, District Nurse, Midwife, Paramedic, Dentist, Approved Social Worker. As a result of these changes, nearly all of the data from the trial is not comparable to that for before October 2015. Some of the differences include: • Call categories A & C have been removed and replaced by colour coding. • Call handlers are allowed up to an additional two minutes to accurately identify both the severity and nature of a patient’s condition (for those calls that are not immediately life threatening), and the clinical resource they require before dispatching an ambulance. • A small proportion of calls that were classed as red 2 calls have been moved to the red category and a proportion of calls have been re-categorised from red 1. This means that comparisons cannot be made between performance against the old red1/2 categories and the current red category. • The changes will result in a reduction in the number of calls received with a time target. • An 8 minute response time target is only applied to red calls and therefore comparisons of the 8 minute target performance cannot be made for before and after 1 October 2015. The total number of calls received prior to 1 October 2015 can still be compared with total calls under the current model. This is done by adding in the GP urgent calls - which were classed as urgent not emergency - prior to December 2011. Therefore only overall call volumes can be compared over time, whilst all other measures during the trial period can only be compared within the trial model. CHANGES DURING THE TRIAL: From 11 November, calls which are originally coded as amber or green and during the initial call taking process the patient deteriorates, these are then re-coded through Professional Question & Answering or by the use of a manual dispatch code/override to red. For these calls, the clock start is re-registered as the time the call is re-coded to red. Calls which are originally coded as red and during the initial call taking process the patient’s condition improves these are then re-coded through Professional Question & Answering or by the use of a manual dispatch code/override to amber or green depending on condition. For these calls clock start is re-registered as the time the call is re-coded to amber or green. The overall impact of this meant that – from 11 to 30 November - an additional 37 calls arrived at the scene within the 8 minute target time, increasing the percentage arriving within the target time by 2.2 percentage points.

This version redacts all sensitive information including who the case was submitted to, who was dispatched, actual location of the incident, and the geocoded location of the incident. The data documented in the table below is the information related to each emergency call that is received by Energy Resources. The data serves as the official record for each call and is used for various things such as determining response time, tracking call volume and ultimately serving as the official record of a response if any questions should arise about how Energy Resources handled an emergency call.

Provide Taoyuan City Government Fire Department 119 reporting line annual indoor telephone reporting times and call duration statistics

If you are in need of emergency shelter space, please call the City of Toronto’s Central Intake line at 416-338-4766 or 1-877-338-3398. This catalogue entry provides two data sets related to calls to Central Intake. Central Intake is a City-operated, 24/7 telephone-based service that offers referrals to emergency shelter and other overnight accommodation, as well as information about other homelessness services. These two data sets provide information about calls received by Central Intake, the outcomes of those calls, and the number of individuals who could not be matched to a shelter space each day. The first, Central Intake Service Queue Data, provides counts of the number of unique individuals who contacted Central Intake to access emergency shelter but were not matched to a shelter space. Generated through Central Intake caseworkers' use of the City's Shelter Management Information System (SMIS), the data are reported as a count for every operational day. The SMIS service queue for Central Intake records when a bed is requested for a caller seeking a shelter space. Those callers who could not be matched to an available space that suits their needs at the time of their call remain in the queue until they can be provided a referral or until the closeout process at the end of the night (i.e. 4:00 a.m.). Service Queue data combines data exported from the Central Intake service queue at 4:00 a.m., with manually coded outcome data based on the review of each individual's SMIS records for the day. SSHA began collecting data on how many people remain unmatched in the service queue over a 24 hour period at the beginning of November 2020. Given the manual nature of the preparation of the data in this data set, this file will be updated on a monthly basis. Data will be reported separately for every operational day in that month. The second data set, Central Intake Call Wrap-Up Codes Data, provides counts of calls answered by Central Intake, classified by the nature of the call. When a call is handled by a caseworker at Central Intake, the caseworker assigns a wrap-up code to the call. This tracking allows for analysis of call trends. Central Intake uses 13 distinct wrap-up codes to code the calls they receive. This data set provides a daily summary of the number of calls received by each call wrap-up code. The data are manually retrieved from the City's call centre database reports. Given the manual nature of the preparation of the data in this data set, this file will be updated on a monthly basis. Data will be reported separately for every operational day in that month. Please note that while the wrap-up codes provide information related to the volume and type of calls answered by Central Intake, the data do not track requests made by unique individuals nor the ultimate outcomes of referrals. Please also note that the previews and Data Features below only show information pertaining to the Central Intake Call Wrap-Up Codes Data dataset.

112 is the EU-wide emergency number. The present survey found that EU citizens remained in general unfamiliar with this number, with just over a quarter (27%) correctly identifying 112 as the number to call anywhere in the EU in the event of an emergency. There has been only a slight increase in Europeans' awareness of the single emergency number 112 over the past 6 years. On the positive side, the awareness of those who travel between Member States is higher (39%). #####The results by volumes are distributed as follows: * Volume A: Countries * Volume AA: Groups of countries * Volume A' (AP): Trends * Volume AA' (AAP): Trends of groups of countries * Volume B: EU/socio-demographics * Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: http://www.gesis.org/en/home/

NOTE: This data does not present a full picture of 311 calls or service requests, in part because of operational and system complexities associated with remote call taking necessitated by the unprecedented volume 311 is handling during the Covid-19 crisis. The City is working to address this issue.
A row level daily report of illegal parking by City vehicle or permit 311 Service Requests starting from 1/30/20.