Kerala Pwd Data Book Pdf
Floriana Grundy
With stunning natural beauty and a resilient spirit, Kerala is harnessing data's power to combat climate change on its journey to an innovative, data-driven future. Together, with strategies, policies, talent, and a keen understanding of societal demands, Kerala is writing a new legacy of resilience - and leading the way.
This GDL Area Profile Report provides an overview of the major social and economic characteristics of the region or country. The figures are derived from the most recent national household survey available at the Global Data Lab. These and many other indicators can also be downloaded freely from the GDL Area Database. Further information on the indicators is available at the bottom of this report and at the GDL website.
The indicators presented in this GDL Profile Report are created by aggregating to the sub-national and national level from representative household surveys. Detailed information on the data and methods used is available here and in Smits (2016).
The Kerala State Spatial Data Infrastructure (KSDI) is an Internet based Geo-spatial Data Directory for the state that facilitates users of the system to share and explore data related to political and administrative boundaries, natural resources, transportation and infrastructure, demography, agro and socio economy etc., of the state.
2012-2022 GOVERNMENT OF INDIA All rights reserved except published datasets/resources and metadata. This Platform is designed, developed and hosted by National Informatics Centre (NIC) , Ministry of Electronics & Information Technology , Government of India. The content published on data.gov.in are owned by the respective Ministry/State/Department/Organization licensed under the Government Open Data License - India .
In the maximum observed flooding map, areas in RED are flooding mapped from Copernicus Sentinel 1 SAR data provided by the European Space Agency. Areas in PURPLE are currently flooded and also flooded in previous years. Areas in BLUE are covered by a reference normal water extent. Areas in LIGHT GRAY are all previously mapped flooding, since 1999.
The observed and model-predicted flooding maps from SAR data can be used effectively for monitoring and analyzing the influence of flood water in a flood prone area. The flood product will help to reduce the flood hazard impact and provide critical information in the process of flood management.
Robert Brakenridge (Dartmouth Flood Observatory at the University of Colorado), Albert Kettner (Dartmouth Flood Observatory at the University of Colorado), Tian Yao (NASA GSFC). Copyright contains modified Copernicus Sentinel data (2018), processed by ESA.
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Objectives: We aim to report the major congenital malformation (MCM) rates for new and old anti-epileptic drugs (AED) exposures during the first trimester of pregnancy in women with epilepsy (WWE).
Methods: We extracted relevant data on drug exposure and malformation rate from the records of a prospective observational registry (Kerala Registry of Epilepsy and Pregnancy) for all completed pregnancies between 1998 and 2019. A comprehensive and uniform criterion with detailed guideline was used for assessment of malformations. We employed generalised linear model to generate adjusted incidence rate ratios (aIRR) of MCM in AED exposed group as compared to AED unexposed group, after adjustment for age and educational status of mothers' and epilepsy classification.
Conclusion: The MCM risk was significantly higher for polytherapy with high dose valproate. It did not differ substantially between different AED monotherapies although point estimate was lowest with lamotrigine. Pregnant women on new AEDs report higher likelihood of GTCS than women on old AEDs during pregnancy.
Disasters lead to breakdown of established Information and Communication Technology (ICT) infrastructure. ICT breakdown obstructs the channel to gather real-time last mile information directly from the disaster-stricken communities and thereby hampers the agility of humanitarian supply chains. This creates a complex, chaotic, uncertain, and restrictive environment for humanitarian relief operations, which struggles for credible information to prioritize and deliver effective relief services. In this paper, we discuss how satellite big data analytics built over real-time weather information, geospatial data and deployed over a cloud-computing platform aided in achieving improved coordination and collaboration between rescue teams for humanitarian relief efforts in the case of 2018 Kerala floods. The analytics platform made available to the stakeholders involved in the rescue operations led to timely logistical planning and execution of rescue missions. The developed platform improved the accuracy of information between the distressed community and the stakeholders involved and thereby increased the agility of humanitarian logistics and relief supply chains. This research proves the utility of fusing data sources that are normally sitting as islands of information using big data analytics to prioritize humanitarian relief operations.
Intelligent process-aware ICTs in the context of humanitarian relief operations refers to the development of tailor-made technology solutions that cater specifically to the needs of disaster management.
We would like to take this opportunity to thank SatSure and its technical team for allowing the authors to access their big data analytics cloud platform. We are also thankful to SatSure for allowing us to conduct thorough observations of the team which built and deployed the platform as a community contribution towards the humanitarian relief operations conducted during the Kerala floods. Specifically, we thank Mr. Prateep Basu, Mr. Pradeep Bisen and Mr. Rashmit Singh Sukhmani for explaining the underlying data structure and technicalities in the process of satellite big data analytics. The case study has also immensely benefitted from the support of Geospoc, SpacePark Kerala and IBM.
This is a set of six ASCII grids describing the peak flood event for six return periods (2-100 years) at each point of the river network in the state of Kerala, India. Estimates were derived in a similar way to the Flood Estimation Handbook* approach. The data is measured in cubic metres per second, and is given on an unprojected resolution of 15 arc-seconds per grid cell. This work was supported by the Natural Environment Research Council as part of the LAWIS programme delivering National Capability. * (Flood Estimation Handbook. Centre for Ecology & Hydrology, 1999, ISBN: 9781906698003) Full details about this dataset can be found at -4706-4f1e-aaf4-fd7769e00db0
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Report Bleaching Kerala, India
5 km Regional Bleaching Heat Stress Maps and Gauges (Version 3.1) NOTE: Alerts displayed on individual Regional Virtual Station (RVS) and Single-Pixel Virtual Station pages still use the heritage bleaching alert level system (which extended to Bleaching Alert Level 2). We are currently updating these systems to reflect the modified bleaching alert levels (now extending to Bleaching Alert Level 5). Until this update is complete, when a Station displays Alert Level 2, we suggest that users consult the Time Series data file that is linked directly below or to the right of the two-year time-series graph. This data file displays the underlying Coral Bleaching HotSpot and Degree Heating Week (DHW) values, from which a user can determine, on any day, what the new, corresponding Bleaching Alert Level should be, if higher than Alert Level 2.
The purpose of these Regional Coral Bleaching Heat Stress Gauges is to provide coral reef ecosystem managers with a comprehensive summary of current satellite-monitored and model-projected bleaching thermal stress conditions to help facilitate timely and effective management actions pertaining to mass coral bleaching.
NOAA Coral Reef Watch (CRW) has developed a set of Heat Stress Gauges to reflect the observed and forecasted bleaching alert level surrounding select islands or reefs. The areas chosen for monitoring are designed to match the 5 km Regional Virtual Station boundaries outlined in black. These gauges are based on CRW's Regional Virtual Station time series data, updated daily, and 0.5-degree Climate Forecast System (CFS)-based Four-Month Coral Bleaching Thermal Stress Outlook, updated weekly.
CRW provides four gauges per Virtual Station that include the current near-real-time coral bleaching thermal stress alert level and the projected alert level for three consecutive 4-week time periods (i.e., the upcoming 1-4 weeks, 5-8 weeks, and 9-12 weeks, hereafter referred to as 4, 8, and 12 weeks, respectively). A map of the region surrounding the island or coastline of interest accompanies the gauges. The image(s) show either the current 7-day maximum Bleaching Alert Area, the current Four-Month Coral Bleaching Outlook for 4, 8, or 12 weeks out, or a composite of the four depending on the user's selection. The gauge corresponding to the map is outlined in black.
Due to the regional nature of the virtual stations, the level displayed on each gauge is a derived value that is not taken from any one data point within the virtual station area. To derive alerts, the daily 90th percentile HotSpot values within the station boundary are used to calculate new Degree Heating Week Values unique to each Regional Virtual Station. From the 90th percentile HotSpot values and newly calculated DHW values, a new Bleaching Alert Area value can be calculated using the same heat stress level table as the standard 5 km Bleaching Alert Area product. The daily 90th percentile HotSpot value and unique DHW and BAA values are stored in the time series data files accessible on the all stations and products page. This methodology keeps the Heat Stress Gauges, Time Series Graphs and Bleaching Email Alerts internally consistent and prevents a few stray warm pixels from exaggerating bleaching risk while reflecting the greatest thermal stress impacting the reefs in the region.
The CFS-based Outlook product used is the 60% likelihood of bleaching thermal stress from the latest Four-Month Coral Bleaching Thermal Stress Outlook. Outlook values displayed are the maximum of a composite of each corresponding 4-week period (i.e., the maximum value from weeks 1-4, 5-8, or 9-12 of the Outlook). A small gray arrow is sometimes present on a gauge indicating a change from the previous gauge reading upon updating. If the arrow is not present then there has been no change since the last update. The current alert gauge and image updates daily while the outlook gauges and images update weekly.
For the current alert level, the area of interest is specified by a black outline surrounding the island(s) or known reef locations on the map. For the 4-, 8-, and 12-week Outlooks, the area of interest is the entire extent of the corresponding map. This increases the area used for the gauge level as one moves from the Bleaching Alert Area to the 4-, 8-, and 12-week Outlooks. The areas covered by the 8- and 12-week Outlooks increase by 2 degrees in every direction from the previous Outlook. The increase in area coverage as the outlook lead-time increases for the gauge calculation is designed to help coral reef managers understand the extent of potentially approaching offshore thermal stress, with increased projection uncertainty factored in over longer time scales. Incorporating an increase in the area at these predefined, projected, time-dependent spatial scales should capture thermal stress events that may contribute to bleaching risk at the depicted coral reefs. In some special cases (i.e, Central America) the extent of the outlook map may encompass two ocean basins that should not collectively be considered when determining the outlook alert. In these cases, a dark gray mask is drawn over the basin that is excluded when calculating the outlook alert to display on the gauges.
The time series graph provides a record of temperature, thermal stress, and bleaching potential and allows for comparisons between years. The SST trace shown depicts the SST value where the HotSpot value is equal to the 90th percentile HotSpot. The minimum and maximum SST range within the Regional Virtual Station boundary is shown as a light gray area behind the SST trace. Click here to access a more detailed description of the time series graphs and data. Search How to cite these products and methods