際際滷shows by User: raedmansour / http://www.slideshare.net/images/logo.gif 際際滷shows by User: raedmansour / Fri, 15 Mar 2019 20:09:13 GMT 際際滷Share feed for 際際滷shows by User: raedmansour Homogeneity of Community Areas in Chicago /slideshow/homogeneity-of-community-areas-in-chicago/136624566 homogeneityofcommunityareasinchicago-190315200913
A presentation by Jonathon Prehn, Elmhurst College at the Chicago Public Health GIS Network Meeting on March 15, 2019. This study investigates how the assumption that Community Areas contain their own homogenous social groups holds throughout the decades of their use. Community areas have been used for boundaries for aggregating data throughout the years. However, when one aggregates data, there is the assumption of homogeneity within the boundary. The violation of this assumption leads to the Modifiable Areal Unit Problem (MAUP), one of the most well-known phenomenon in geography. The community areas of Chicago is a special delineation first introduced by the University of Chicagos Local Community Research Committee between 1924 and 1930, however, speculation is considerable that homogeneity no longer exists within most of these designated areas. Ten Community Areas were selected for this research, and spatial statistical tests such as the Local Indicator for Spatial Autocorrelation (LISA) were applied for determining homogeneity across five decades of demographic census data. The results provide evidence that the community areas of Chicago are no longer viable as boundaries for social science research, for descriptive statistics on demographic data would not accurately represent the population residing in the defined area. Significantly, these community areas are still used in tabulation of health statistics by The Chicago Department of Public Health and in recent sociological research.]]>
A presentation by Jonathon Prehn, Elmhurst College at the Chicago Public Health GIS Network Meeting on March 15, 2019. This study investigates how the assumption that Community Areas contain their own homogenous social groups holds throughout the decades of their use. Community areas have been used for boundaries for aggregating data throughout the years. However, when one aggregates data, there is the assumption of homogeneity within the boundary. The violation of this assumption leads to the Modifiable Areal Unit Problem (MAUP), one of the most well-known phenomenon in geography. The community areas of Chicago is a special delineation first introduced by the University of Chicagos Local Community Research Committee between 1924 and 1930, however, speculation is considerable that homogeneity no longer exists within most of these designated areas. Ten Community Areas were selected for this research, and spatial statistical tests such as the Local Indicator for Spatial Autocorrelation (LISA) were applied for determining homogeneity across five decades of demographic census data. The results provide evidence that the community areas of Chicago are no longer viable as boundaries for social science research, for descriptive statistics on demographic data would not accurately represent the population residing in the defined area. Significantly, these community areas are still used in tabulation of health statistics by The Chicago Department of Public Health and in recent sociological research.]]> Fri, 15 Mar 2019 20:09:13 GMT /slideshow/homogeneity-of-community-areas-in-chicago/136624566 raedmansour@slideshare.net(raedmansour) Homogeneity of Community Areas in Chicago raedmansour A presentation by Jonathon Prehn, Elmhurst College at the Chicago Public Health GIS Network Meeting on March 15, 2019. This study investigates how the assumption that Community Areas contain their own homogenous social groups holds throughout the decades of their use. Community areas have been used for boundaries for aggregating data throughout the years. However, when one aggregates data, there is the assumption of homogeneity within the boundary. The violation of this assumption leads to the Modifiable Areal Unit Problem (MAUP), one of the most well-known phenomenon in geography. The community areas of Chicago is a special delineation first introduced by the University of Chicagos Local Community Research Committee between 1924 and 1930, however, speculation is considerable that homogeneity no longer exists within most of these designated areas. Ten Community Areas were selected for this research, and spatial statistical tests such as the Local Indicator for Spatial Autocorrelation (LISA) were applied for determining homogeneity across five decades of demographic census data. The results provide evidence that the community areas of Chicago are no longer viable as boundaries for social science research, for descriptive statistics on demographic data would not accurately represent the population residing in the defined area. Significantly, these community areas are still used in tabulation of health statistics by The Chicago Department of Public Health and in recent sociological research. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/homogeneityofcommunityareasinchicago-190315200913-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A presentation by Jonathon Prehn, Elmhurst College at the Chicago Public Health GIS Network Meeting on March 15, 2019. This study investigates how the assumption that Community Areas contain their own homogenous social groups holds throughout the decades of their use. Community areas have been used for boundaries for aggregating data throughout the years. However, when one aggregates data, there is the assumption of homogeneity within the boundary. The violation of this assumption leads to the Modifiable Areal Unit Problem (MAUP), one of the most well-known phenomenon in geography. The community areas of Chicago is a special delineation first introduced by the University of Chicagos Local Community Research Committee between 1924 and 1930, however, speculation is considerable that homogeneity no longer exists within most of these designated areas. Ten Community Areas were selected for this research, and spatial statistical tests such as the Local Indicator for Spatial Autocorrelation (LISA) were applied for determining homogeneity across five decades of demographic census data. The results provide evidence that the community areas of Chicago are no longer viable as boundaries for social science research, for descriptive statistics on demographic data would not accurately represent the population residing in the defined area. Significantly, these community areas are still used in tabulation of health statistics by The Chicago Department of Public Health and in recent sociological research.
Homogeneity of Community Areas in Chicago from Raed Mansour
]]> 184 4 https://cdn.slidesharecdn.com/ss_thumbnails/homogeneityofcommunityareasinchicago-190315200913-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 An Open Spatial Systems Framework for Place-Based Decision-Making /slideshow/an-open-spatial-systems-framework-for-placebased-decisionmaking/60976050 phgisapril2016-160415232649
Marynia Kolak, PhD Candidate from Arizona State University's GeoDa Center presented on April 15, 2016 for the Chicago GIS in Public Health group at the Chicago Department of Public Health (CDPH). She presented on the Healthy Access, Health Regions project, a collaboration of CDPH and the GeoDa Center at Arizona State. See abstract below: The Healthy Access, Health Regions project is a collaboration with the GeoDa Center, the Chicago Department of Public Health, and others to build a customized open-source web application for data integration, exploratory analysis, and decision-making. It seeks to push GIS to the frontiers of spatial data science, where space serves as the place for integrating research design and methodology, data infrastructure, and learning. This project works on integrating data on-the-fly and working towards dynamic visualization and analysis in a spatial big data infrastructure. Remotely managed resource and health provider data are streamed into the application for analysis. Functions are encoded to evaluate service areas and explore socioeconomic and community health outcome data. Another aspect integrates an implementation of the max-p algorithm to develop data-driven regions for exploration and analysis. The next phase of development will better integrate dynamic analytics and simulation and enhanced user experience design. This application seeks to not only test feasibility of data integration and analysis support, but also serve as a collaboratively developed and community-driven structure.]]>
Marynia Kolak, PhD Candidate from Arizona State University's GeoDa Center presented on April 15, 2016 for the Chicago GIS in Public Health group at the Chicago Department of Public Health (CDPH). She presented on the Healthy Access, Health Regions project, a collaboration of CDPH and the GeoDa Center at Arizona State. See abstract below: The Healthy Access, Health Regions project is a collaboration with the GeoDa Center, the Chicago Department of Public Health, and others to build a customized open-source web application for data integration, exploratory analysis, and decision-making. It seeks to push GIS to the frontiers of spatial data science, where space serves as the place for integrating research design and methodology, data infrastructure, and learning. This project works on integrating data on-the-fly and working towards dynamic visualization and analysis in a spatial big data infrastructure. Remotely managed resource and health provider data are streamed into the application for analysis. Functions are encoded to evaluate service areas and explore socioeconomic and community health outcome data. Another aspect integrates an implementation of the max-p algorithm to develop data-driven regions for exploration and analysis. The next phase of development will better integrate dynamic analytics and simulation and enhanced user experience design. This application seeks to not only test feasibility of data integration and analysis support, but also serve as a collaboratively developed and community-driven structure.]]> Fri, 15 Apr 2016 23:26:48 GMT /slideshow/an-open-spatial-systems-framework-for-placebased-decisionmaking/60976050 raedmansour@slideshare.net(raedmansour) An Open Spatial Systems Framework for Place-Based Decision-Making raedmansour Marynia Kolak, PhD Candidate from Arizona State University's GeoDa Center presented on April 15, 2016 for the Chicago GIS in Public Health group at the Chicago Department of Public Health (CDPH). She presented on the Healthy Access, Health Regions project, a collaboration of CDPH and the GeoDa Center at Arizona State. See abstract below: The Healthy Access, Health Regions project is a collaboration with the GeoDa Center, the Chicago Department of Public Health, and others to build a customized open-source web application for data integration, exploratory analysis, and decision-making. It seeks to push GIS to the frontiers of spatial data science, where space serves as the place for integrating research design and methodology, data infrastructure, and learning. This project works on integrating data on-the-fly and working towards dynamic visualization and analysis in a spatial big data infrastructure. Remotely managed resource and health provider data are streamed into the application for analysis. Functions are encoded to evaluate service areas and explore socioeconomic and community health outcome data. Another aspect integrates an implementation of the max-p algorithm to develop data-driven regions for exploration and analysis. The next phase of development will better integrate dynamic analytics and simulation and enhanced user experience design. This application seeks to not only test feasibility of data integration and analysis support, but also serve as a collaboratively developed and community-driven structure. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phgisapril2016-160415232649-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Marynia Kolak, PhD Candidate from Arizona State University&#39;s GeoDa Center presented on April 15, 2016 for the Chicago GIS in Public Health group at the Chicago Department of Public Health (CDPH). She presented on the Healthy Access, Health Regions project, a collaboration of CDPH and the GeoDa Center at Arizona State. See abstract below: The Healthy Access, Health Regions project is a collaboration with the GeoDa Center, the Chicago Department of Public Health, and others to build a customized open-source web application for data integration, exploratory analysis, and decision-making. It seeks to push GIS to the frontiers of spatial data science, where space serves as the place for integrating research design and methodology, data infrastructure, and learning. This project works on integrating data on-the-fly and working towards dynamic visualization and analysis in a spatial big data infrastructure. Remotely managed resource and health provider data are streamed into the application for analysis. Functions are encoded to evaluate service areas and explore socioeconomic and community health outcome data. Another aspect integrates an implementation of the max-p algorithm to develop data-driven regions for exploration and analysis. The next phase of development will better integrate dynamic analytics and simulation and enhanced user experience design. This application seeks to not only test feasibility of data integration and analysis support, but also serve as a collaboratively developed and community-driven structure.
An Open Spatial Systems Framework for Place-Based Decision-Making from Raed Mansour
]]> 507 9 https://cdn.slidesharecdn.com/ss_thumbnails/phgisapril2016-160415232649-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 APHA Presentation: Using Predictive Analytics for West Nile Disease Prevention /slideshow/apha-presentation-using-predictive-analytics-for-west-nile-disease-prevention/56063545 aphawnvpredictive2015-151211182344
Presentation at the 2015 American Public Health Association Annual Meeting in Chicago. Since 2004, the City of Chicago has had a comprehensive surveillance and control program to address West Nile virus (WNV). Environmental surveillance has included: the collection of mosquitoes from traps located throughout the city; the identification and sorting of mosquitoes collected from these traps; and the testing of specific species of mosquitoes for WNV. Environmental control measures have included targeted adulticiding efforts. This project will identify factors associated with the presence of West Nile virus (WNV) in mosquitoes and determine the effectiveness of mosquito control measures. Information gained will help the City of Chicago better target its surveillance, prevention and control efforts An open competition to determine the best model is being planned by Kaggle who will be hosting the competition in partnership with Robert Wood Johnson Foundation and CDPH. CDPH will provide data and technical support. There will be 8 years of public health data incorporated into the model that will be tested and potentially incorporated into business practice. Full Abstract: https://apha.confex.com/apha/143am/webprogram/Paper335111.html]]>
Presentation at the 2015 American Public Health Association Annual Meeting in Chicago. Since 2004, the City of Chicago has had a comprehensive surveillance and control program to address West Nile virus (WNV). Environmental surveillance has included: the collection of mosquitoes from traps located throughout the city; the identification and sorting of mosquitoes collected from these traps; and the testing of specific species of mosquitoes for WNV. Environmental control measures have included targeted adulticiding efforts. This project will identify factors associated with the presence of West Nile virus (WNV) in mosquitoes and determine the effectiveness of mosquito control measures. Information gained will help the City of Chicago better target its surveillance, prevention and control efforts An open competition to determine the best model is being planned by Kaggle who will be hosting the competition in partnership with Robert Wood Johnson Foundation and CDPH. CDPH will provide data and technical support. There will be 8 years of public health data incorporated into the model that will be tested and potentially incorporated into business practice. Full Abstract: https://apha.confex.com/apha/143am/webprogram/Paper335111.html]]> Fri, 11 Dec 2015 18:23:44 GMT /slideshow/apha-presentation-using-predictive-analytics-for-west-nile-disease-prevention/56063545 raedmansour@slideshare.net(raedmansour) APHA Presentation: Using Predictive Analytics for West Nile Disease Prevention raedmansour Presentation at the 2015 American Public Health Association Annual Meeting in Chicago. Since 2004, the City of Chicago has had a comprehensive surveillance and control program to address West Nile virus (WNV). Environmental surveillance has included: the collection of mosquitoes from traps located throughout the city; the identification and sorting of mosquitoes collected from these traps; and the testing of specific species of mosquitoes for WNV. Environmental control measures have included targeted adulticiding efforts. This project will identify factors associated with the presence of West Nile virus (WNV) in mosquitoes and determine the effectiveness of mosquito control measures. Information gained will help the City of Chicago better target its surveillance, prevention and control efforts An open competition to determine the best model is being planned by Kaggle who will be hosting the competition in partnership with Robert Wood Johnson Foundation and CDPH. CDPH will provide data and technical support. There will be 8 years of public health data incorporated into the model that will be tested and potentially incorporated into business practice. Full Abstract: https://apha.confex.com/apha/143am/webprogram/Paper335111.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aphawnvpredictive2015-151211182344-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at the 2015 American Public Health Association Annual Meeting in Chicago. Since 2004, the City of Chicago has had a comprehensive surveillance and control program to address West Nile virus (WNV). Environmental surveillance has included: the collection of mosquitoes from traps located throughout the city; the identification and sorting of mosquitoes collected from these traps; and the testing of specific species of mosquitoes for WNV. Environmental control measures have included targeted adulticiding efforts. This project will identify factors associated with the presence of West Nile virus (WNV) in mosquitoes and determine the effectiveness of mosquito control measures. Information gained will help the City of Chicago better target its surveillance, prevention and control efforts An open competition to determine the best model is being planned by Kaggle who will be hosting the competition in partnership with Robert Wood Johnson Foundation and CDPH. CDPH will provide data and technical support. There will be 8 years of public health data incorporated into the model that will be tested and potentially incorporated into business practice. Full Abstract: https://apha.confex.com/apha/143am/webprogram/Paper335111.html
APHA Presentation: Using Predictive Analytics for West Nile Disease Prevention from Raed Mansour
]]> 443 6 https://cdn.slidesharecdn.com/ss_thumbnails/aphawnvpredictive2015-151211182344-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Pinterest for Health Webinar /slideshow/pinterest-for-health-webinar/50875165 pinterestforhealthwebinarmansour-150724053351-lva1-app6891
UC Berkeley School of Public Health Center for Health Leadership webinar: New Media Best Practices Using Pinterest for Health. View entire webinar here: https://cc.readytalk.com/cc/s/meetingArchive?eventId=9dbhjh441mh1]]>
UC Berkeley School of Public Health Center for Health Leadership webinar: New Media Best Practices Using Pinterest for Health. View entire webinar here: https://cc.readytalk.com/cc/s/meetingArchive?eventId=9dbhjh441mh1]]> Fri, 24 Jul 2015 05:33:51 GMT /slideshow/pinterest-for-health-webinar/50875165 raedmansour@slideshare.net(raedmansour) Pinterest for Health Webinar raedmansour UC Berkeley School of Public Health Center for Health Leadership webinar: New Media Best Practices Using Pinterest for Health. View entire webinar here: https://cc.readytalk.com/cc/s/meetingArchive?eventId=9dbhjh441mh1 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pinterestforhealthwebinarmansour-150724053351-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> UC Berkeley School of Public Health Center for Health Leadership webinar: New Media Best Practices Using Pinterest for Health. View entire webinar here: https://cc.readytalk.com/cc/s/meetingArchive?eventId=9dbhjh441mh1
Pinterest for Health Webinar from Raed Mansour
]]> 410 3 https://cdn.slidesharecdn.com/ss_thumbnails/pinterestforhealthwebinarmansour-150724053351-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Social Media and Machine Learning in FoodBorne Illness /slideshow/raed-mansour-foodborne-iit-presentation/48586347 raedmansourfoodborneiitpresentation-150526004306-lva1-app6891
Raed Mansour presenting at the Illinois Institute of Technology Computer Science Seminar on the use of Machine Learning in Social Media as used by the Chicago Department of Public Health's FoodBorne Chicago app.]]>
Raed Mansour presenting at the Illinois Institute of Technology Computer Science Seminar on the use of Machine Learning in Social Media as used by the Chicago Department of Public Health's FoodBorne Chicago app.]]> Tue, 26 May 2015 00:43:06 GMT /slideshow/raed-mansour-foodborne-iit-presentation/48586347 raedmansour@slideshare.net(raedmansour) Social Media and Machine Learning in FoodBorne Illness raedmansour Raed Mansour presenting at the Illinois Institute of Technology Computer Science Seminar on the use of Machine Learning in Social Media as used by the Chicago Department of Public Health's FoodBorne Chicago app. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/raedmansourfoodborneiitpresentation-150526004306-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Raed Mansour presenting at the Illinois Institute of Technology Computer Science Seminar on the use of Machine Learning in Social Media as used by the Chicago Department of Public Health&#39;s FoodBorne Chicago app.
Social Media and Machine Learning in FoodBorne Illness from Raed Mansour
]]> 1292 3 https://cdn.slidesharecdn.com/ss_thumbnails/raedmansourfoodborneiitpresentation-150526004306-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Innovation Transforming Public Health in Chicago /slideshow/innovation-transforming-public-health-in-chicago/43157736 innovationtransformingpublichealthinchicago-150102162115-conversion-gate01
Big cities continue to be centers for innovative solutions and services. Governments are quickly identifying opportunities to take advantage of this energy and revolutionize the means by which they deliver services to the public. The governmental public health sector is rapidly evolving in this respect, and Chicago is an emerging example of some of the changes to come. Governments are gradually adopting innovative informatics and big data tools and strategies, led by pioneering jurisdictions that are piecing together the standards, policy frameworks, and leadership structures fundamental to effective analytics use. They give an enticing glimpse of the technology's potential and a sense of the challenges that stand in the way. This is a rapidly evolving environment, and cities can work with partners to capitalize on the innovative energies of civic tech communities, health care systems, and emerging markets to introduce new methods to solve old problems.]]>
Big cities continue to be centers for innovative solutions and services. Governments are quickly identifying opportunities to take advantage of this energy and revolutionize the means by which they deliver services to the public. The governmental public health sector is rapidly evolving in this respect, and Chicago is an emerging example of some of the changes to come. Governments are gradually adopting innovative informatics and big data tools and strategies, led by pioneering jurisdictions that are piecing together the standards, policy frameworks, and leadership structures fundamental to effective analytics use. They give an enticing glimpse of the technology's potential and a sense of the challenges that stand in the way. This is a rapidly evolving environment, and cities can work with partners to capitalize on the innovative energies of civic tech communities, health care systems, and emerging markets to introduce new methods to solve old problems.]]> Fri, 02 Jan 2015 16:21:15 GMT /slideshow/innovation-transforming-public-health-in-chicago/43157736 raedmansour@slideshare.net(raedmansour) Innovation Transforming Public Health in Chicago raedmansour Big cities continue to be centers for innovative solutions and services. Governments are quickly identifying opportunities to take advantage of this energy and revolutionize the means by which they deliver services to the public. The governmental public health sector is rapidly evolving in this respect, and Chicago is an emerging example of some of the changes to come. Governments are gradually adopting innovative informatics and big data tools and strategies, led by pioneering jurisdictions that are piecing together the standards, policy frameworks, and leadership structures fundamental to effective analytics use. They give an enticing glimpse of the technology's potential and a sense of the challenges that stand in the way. This is a rapidly evolving environment, and cities can work with partners to capitalize on the innovative energies of civic tech communities, health care systems, and emerging markets to introduce new methods to solve old problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/innovationtransformingpublichealthinchicago-150102162115-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Big cities continue to be centers for innovative solutions and services. Governments are quickly identifying opportunities to take advantage of this energy and revolutionize the means by which they deliver services to the public. The governmental public health sector is rapidly evolving in this respect, and Chicago is an emerging example of some of the changes to come. Governments are gradually adopting innovative informatics and big data tools and strategies, led by pioneering jurisdictions that are piecing together the standards, policy frameworks, and leadership structures fundamental to effective analytics use. They give an enticing glimpse of the technology&#39;s potential and a sense of the challenges that stand in the way. This is a rapidly evolving environment, and cities can work with partners to capitalize on the innovative energies of civic tech communities, health care systems, and emerging markets to introduce new methods to solve old problems.
Innovation Transforming Public Health in Chicago from Raed Mansour
]]> 468 3 https://cdn.slidesharecdn.com/ss_thumbnails/innovationtransformingpublichealthinchicago-150102162115-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds document 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 CDC MMWR Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014 /slideshow/cdc-mmwr-health-department-use-of-social-media-to-identify-foodborne-illness-chicago-illinois-20132014/38074792 cdcmmwrfoodbornechicago-140817195410-phpapp01
Harris J.K., Mansour R., Choucair B., Olson J., Nissen C., Bhatt J. Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014. MMWR 2014;63:681-685.]]>
Harris J.K., Mansour R., Choucair B., Olson J., Nissen C., Bhatt J. Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014. MMWR 2014;63:681-685.]]> Sun, 17 Aug 2014 19:54:10 GMT /slideshow/cdc-mmwr-health-department-use-of-social-media-to-identify-foodborne-illness-chicago-illinois-20132014/38074792 raedmansour@slideshare.net(raedmansour) CDC MMWR Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014 raedmansour Harris J.K., Mansour R., Choucair B., Olson J., Nissen C., Bhatt J. Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014. MMWR 2014;63:681-685. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cdcmmwrfoodbornechicago-140817195410-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Harris J.K., Mansour R., Choucair B., Olson J., Nissen C., Bhatt J. Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014. MMWR 2014;63:681-685.
CDC MMWR Health Department Use of Social Media to Identify Foodborne Illness Chicago, Illinois, 20132014 from Raed Mansour
]]> 668 10 https://cdn.slidesharecdn.com/ss_thumbnails/cdcmmwrfoodbornechicago-140817195410-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 FoodBorne Chicago Infographic /slideshow/foodborne-chicago-infographic/28122860 foodbornechicagoinfographicfinal2-131111105345-phpapp01
FoodborneChicago.org is a new way to help protect Chicago from food poisoning. When residents use Twitter to post about their illness, we use Machine Learning to pinpoint real cases of food poisoning and reply back with a link that allows the resident to submit a report to the Chicago Department of Public Health to investigate the establishment for possible food related illness.]]>
FoodborneChicago.org is a new way to help protect Chicago from food poisoning. When residents use Twitter to post about their illness, we use Machine Learning to pinpoint real cases of food poisoning and reply back with a link that allows the resident to submit a report to the Chicago Department of Public Health to investigate the establishment for possible food related illness.]]> Mon, 11 Nov 2013 10:53:45 GMT /slideshow/foodborne-chicago-infographic/28122860 raedmansour@slideshare.net(raedmansour) FoodBorne Chicago Infographic raedmansour FoodborneChicago.org is a new way to help protect Chicago from food poisoning. When residents use Twitter to post about their illness, we use Machine Learning to pinpoint real cases of food poisoning and reply back with a link that allows the resident to submit a report to the Chicago Department of Public Health to investigate the establishment for possible food related illness. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/foodbornechicagoinfographicfinal2-131111105345-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> FoodborneChicago.org is a new way to help protect Chicago from food poisoning. When residents use Twitter to post about their illness, we use Machine Learning to pinpoint real cases of food poisoning and reply back with a link that allows the resident to submit a report to the Chicago Department of Public Health to investigate the establishment for possible food related illness.
FoodBorne Chicago Infographic from Raed Mansour
]]> 305 2 https://cdn.slidesharecdn.com/ss_thumbnails/foodbornechicagoinfographicfinal2-131111105345-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds infographic Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 CDPH Social Media Metrics Highlights 2012 /slideshow/cdph-social-media-metrics-highlights-2012/17381926 cdphsocialmediametricshighlights2012-130319163445-phpapp01
]]>
]]> Tue, 19 Mar 2013 16:34:45 GMT /slideshow/cdph-social-media-metrics-highlights-2012/17381926 raedmansour@slideshare.net(raedmansour) CDPH Social Media Metrics Highlights 2012 raedmansour <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cdphsocialmediametricshighlights2012-130319163445-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
CDPH Social Media Metrics Highlights 2012 from Raed Mansour
]]> 301 2 https://cdn.slidesharecdn.com/ss_thumbnails/cdphsocialmediametricshighlights2012-130319163445-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Social Media Week 2012 - #SMWChiHealth /slideshow/social-media-week-2012-17250854/17250854 socialmediaweek-130316013830-phpapp01
"Can Social Media Improve Population Health?" Session]]>
"Can Social Media Improve Population Health?" Session]]> Sat, 16 Mar 2013 01:38:29 GMT /slideshow/social-media-week-2012-17250854/17250854 raedmansour@slideshare.net(raedmansour) Social Media Week 2012 - #SMWChiHealth raedmansour "Can Social Media Improve Population Health?" Session <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/socialmediaweek-130316013830-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> &quot;Can Social Media Improve Population Health?&quot; Session
Social Media Week 2012 - #SMWChiHealth from Raed Mansour
]]> 191 2 https://cdn.slidesharecdn.com/ss_thumbnails/socialmediaweek-130316013830-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 2011 APHA HA Section Facebook Page Insights /slideshow/2011-apha-ha-section-facebook-page-insights-10358376/10358376 fbhapagestats-111128003845-phpapp01
2011 American Public Health Association Health Administration Section Facebook Page Insights]]>
2011 American Public Health Association Health Administration Section Facebook Page Insights]]> Mon, 28 Nov 2011 00:38:44 GMT /slideshow/2011-apha-ha-section-facebook-page-insights-10358376/10358376 raedmansour@slideshare.net(raedmansour) 2011 APHA HA Section Facebook Page Insights raedmansour 2011 American Public Health Association Health Administration Section Facebook Page Insights <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fbhapagestats-111128003845-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 2011 American Public Health Association Health Administration Section Facebook Page Insights
2011 APHA HA Section Facebook Page Insights from Raed Mansour
]]> 303 5 https://cdn.slidesharecdn.com/ss_thumbnails/fbhapagestats-111128003845-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 2011 HA Section Membership Survey /slideshow/2011-membership-survey/10149807 membershipsurvey-111114025253-phpapp01
APHA Health Administration Section Survey Results.]]>
APHA Health Administration Section Survey Results.]]> Mon, 14 Nov 2011 02:52:50 GMT /slideshow/2011-membership-survey/10149807 raedmansour@slideshare.net(raedmansour) 2011 HA Section Membership Survey raedmansour APHA Health Administration Section Survey Results. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/membershipsurvey-111114025253-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> APHA Health Administration Section Survey Results.
2011 HA Section Membership Survey from Raed Mansour
]]> 246 124 https://cdn.slidesharecdn.com/ss_thumbnails/membershipsurvey-111114025253-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 2011 APHA HA LinkedIn Group Stats /slideshow/2011-apha-ha-linkedin-group-stats/10144033 linkedinhagroupstats-111113162315-phpapp02
American Public Health Association Health Administration Section LinkedIn Group Statistics.]]>
American Public Health Association Health Administration Section LinkedIn Group Statistics.]]> Sun, 13 Nov 2011 16:23:13 GMT /slideshow/2011-apha-ha-linkedin-group-stats/10144033 raedmansour@slideshare.net(raedmansour) 2011 APHA HA LinkedIn Group Stats raedmansour American Public Health Association Health Administration Section LinkedIn Group Statistics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/linkedinhagroupstats-111113162315-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> American Public Health Association Health Administration Section LinkedIn Group Statistics.
2011 APHA HA LinkedIn Group Stats from Raed Mansour
]]> 199 124 https://cdn.slidesharecdn.com/ss_thumbnails/linkedinhagroupstats-111113162315-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-raedmansour-48x48.jpg?cb=1576201978 Director, Office of Innovation https://cdn.slidesharecdn.com/ss_thumbnails/homogeneityofcommunityareasinchicago-190315200913-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/homogeneity-of-community-areas-in-chicago/136624566 Homogeneity of Communi... https://cdn.slidesharecdn.com/ss_thumbnails/phgisapril2016-160415232649-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/an-open-spatial-systems-framework-for-placebased-decisionmaking/60976050 An Open Spatial System... https://cdn.slidesharecdn.com/ss_thumbnails/aphawnvpredictive2015-151211182344-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/apha-presentation-using-predictive-analytics-for-west-nile-disease-prevention/56063545 APHA Presentation: Usi...