Data Visualization / Business Intelligence Analyst Volunteer Wildfire Prevention

Data Visualization / Business Intelligence Analyst Volunteer Wildfire Prevention General geographic and demographic characteristics: total area, perimeter, climate zone, seasonal weather conditions (temperature, humidity, atmospheric pressure, number of sunny days, wind conditions, etc.), topography descriptors, regional population statistics, crime rate, etc. Size of developed area, size of a grassland, length of the common border between the developed area and the grassland, grass type. Fire history in the region: fire occurrences in the past, dates of occurrences, duration, intensity of fires. Fire susceptible region (for example, live or dead forest) that is connected to the region of interest is the threat to the region. Its fire characteristics are particularly important ones. Among them are size, perimeter and factors that affect spread of fire such as fuel type (trees, debris, etc.) and terrain features. Exposure variables include number of properties in a region, residential households and businesses, values’ statistics (minimum, maximum, mean and median costs), income statistics for businesses and residents, other financial information that affects loss evaluation. Insurance variables: how many properties are insured, premium policy statistics (minimum, maximum, average, and medium prices), insurance values statistics, major insurers in a region, etc. History of losses due-to wildfires. These records describe quantities which can be associated with our model’s outcomes. -Prevention derivatives is driven by the thesis that there is an under-valuation of passive risk (or the cost of inaction) and an under-prioritization of positive risk. Correspondingly for wildfires as an example, there is an under-recognition of the potential shared value upside of preventative action through social innovation and social interventions (such as goats & sheep that prevent wildfires). CrowdDoing.world's aim is to guarantee positive risk through leveraging existing liabilities to allow for the implications of prescriptive analytics to be financed. The under-pricing of passive risk means that liabilities are treated as either costs of doing business or un-predictable risks even for entirely preventable risks. Risk management offices have been too biased towards avoiding taking the wrong risks rather than ensuring that institutions make their own luck by seizing the abundant positive risk opportunities in social innovation. Meanwhile, the bias against positive risk leaves social innovations not to get adopted even if there would be remarkable benefits to all stakeholders if they were adopted Tools - Tableau, Power BI, Excel BI, R, Python, PowerPoint Knowledge or Interest in any one or more: Dashboard Development Descriptive Analytics Charting / Graphing Exploratory Analysis GIS Data visualization (Mapping) Insight generation Expectations: Identify/Collect data relevant to wildfire Impact Generate Insights from currently available data Present your work Work cross-functionally Traits: Visually inclined, analytical, creative, curious, can conduct analyses independently or with minimal supervision. For questions and correspondence regarding codesigning a perfect volunteer role for yourself in the CrowdDoing systems change venture lab please email: "Journey.ikigai@crowddoing.world" Watch our video to learn more: Systemic change by CrowdDoing