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%
% This script estimate the average hazard for the baseline period 2007-2011
% as reported in:                                  
%                                                                              
% Daniel Nohrstedt, Maurizio Mazzoleni, Charles F. Parker, and Giuliano Di
% Baldassarre. Policy change after natural hazard
% events, Nature Communication
%                                                                              
% This script was prepared by Maurizio Mazzoleni                 
% Uppsala University, 2020                                                  
% mail to: maurizio.mazzoleni@geo.uu.se 
%
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%%Estimate hazard indicators for 2007-2011 based on the HFA countries 
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a=1;
for j=1:size(countryChange,1)  %loop based on country vector from policy change
    temp_country=countryChange{j,1};
    temp_namecountry=countryChange{j,2};
    position=find(strcmp(emdatcountries_1970_2018, temp_country)==1);   %which position in EMDATA is the country 'pais'
    if (~isempty(position))  %if the country 'pais' is
        year_temp=EM_DATA_1970_2018_Disaster{10,position+1};
      %%Any disasters occurring during 2007-N until 2009
        rows=find(year_temp>=lower_limit & year_temp<=higher_limit);
        if (~isempty(rows))  %if there were nature induced disasters, take the indicators
            country_code_vec{1,a}=temp_country;
            country_name_vec{1,a}=temp_namecountry;
          %%Death
            temp_death=EM_DATA_1970_2018_Disaster{4,position+1}(rows,1);  
            temp_affected=EM_DATA_1970_2018_Disaster{8,position+1}(rows,1);
            temp_damage=EM_DATA_1970_2018_Disaster{9,position+1}(rows,1);
            temp_occurrence=EM_DATA_1970_2018_Disaster{3,position+1}(rows,1);
            Summary_data(1,a)=nanmean(temp_death);
            Summary_data(2,a)=nanmean(temp_affected);
            Summary_data(3,a)=nanmean(temp_damage);
            Summary_data(4,a)=nanmean(temp_occurrence);
            clear temp_damage; clear temp_death; clear temp_damage; clear temp_occurrence
            a=a+1;
        end
    end
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%Merge policy and hazards data
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a=1;
for s=1:size(Summary_data,2)   %arranges the matrix to be used in correlation
    country=country_code_vec{1,s};
    name_country=country_name_vec{1,s};
    position_HFA=find(strcmp(countryChange, country)==1);
    if (~isempty(position_HFA))
        analysis_country{a,1}=country;
        analysis_country{a,2}=name_country;
        Analysis_0711(1:4,a)=Summary_data(:,s);  %
        Analysis_0711(5:9,a)=HFAChange(position_HFA,:);
        a=a+1;
    end
end



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%GDP estimate for 2015
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Income_2015=zeros(size(analysis_country,1),2);
for tt=1:size(analysis_country,1)  %loop based on country vector from policy change
    temp_country=analysis_country{tt,1};
    positionGDP=find(strcmp(Income_countries, temp_country)==1); %position in GDP data is the country 'pais'
    Income_classes=Income_classes_values(positionGDP, 9);
    Income_2015(tt,2)=Income_classes;
end