<jupyter_start><jupyter_text>Linked Choropleth Map Visulaizations using ACM with built-in data  1. Adaptive Choropleth Mapper<jupyter_code>param = {
        'title': "Adaptive Choropleth Mapper",
        'filename_suffix': "Adaptive_Choropleth_Maps",
        'state_fips': None,    #fips code is avaiable for every US state at https://github.com/suhanmappingideas/geosnap-viz/blob/master/state_id.csv
        'msa_fips': "16980",   #For more options: http://su-gis.iptime.org/LNE/pick_POI.html
        'county_fips': None,   #county code 
        'years': [1980, 1990, 2000, 2010], #Available years: 1970, 1980, 1990, 2000, 2010
        'label': "short_name", #Pick variable,short_name,full_name from template/conversion_table_codebook.csv         
        'variables': [         #enter variable names of the column you selected above.
            "p_nonhisp_white_persons",
            "p_nonhisp_black_persons",
            "p_hispanic_persons",
            "p_asian_persons",
            "p_foreign_born_pop",
            "p_edu_college_greater",
            "p_unemployment_rate",
            "p_employed_manufacturing",
            "p_poverty_rate",
            "p_vacant_housing_units",
            "p_owner_occupied_units",
            "p_housing_units_multiunit_structures",
            "median_home_value",
            "p_structures_30_old",
            "p_household_recent_move",
            "p_persons_under_18",
            "p_persons_over_60",            
        ],
         'chart': "Stacked Chart",  #Comment out if you do not want to visualize this chart     
}
Adaptive_Choropleth_Mapper_viz(param)<jupyter_output><empty_output><jupyter_text>2. Adaptive Choropleth Mapper with Correlogram<jupyter_code>param = {
        'title': "Adaptive Choropleth Mapper with Correlogram",
        'filename_suffix': "Adaptive_Choropleth_Maps_correlogram",
        'state_fips': None,    #fips code is avaiable for every US state at https://github.com/suhanmappingideas/geosnap-viz/blob/master/state_id.csv
        'msa_fips': "16980",   #For more options: http://su-gis.iptime.org/LNE/pick_POI.html
        'county_fips': None,   #county code 
        'year': 2010, #Available years: 1970, 1980, 1990, 2000, 2010
        'NumOfMaps': 4,
        'label': "short_name", #Pick variable,short_name,full_name from template/conversion_table_codebook.csv         
        'variables': [         #enter variable names of the column you selected above.
            "p_nonhisp_white_persons",
            "p_nonhisp_black_persons",
            "p_hispanic_persons",
            "p_asian_persons",
            "p_foreign_born_pop",
            "p_edu_college_greater",
            "p_unemployment_rate",
            "p_employed_manufacturing",
            "p_poverty_rate",
            "p_vacant_housing_units",
            "p_owner_occupied_units",
            "p_housing_units_multiunit_structures",
            "median_home_value",
            "p_structures_30_old",
            "p_household_recent_move",
            "p_persons_under_18",
            "p_persons_over_60",    
        ],
        'chart': "Correlogram",
}
Adaptive_Choropleth_Mapper_viz(param)<jupyter_output><empty_output><jupyter_text>3. Adaptive Choropleth Mapper with Scatter Plot<jupyter_code>param = {
        'title': "Adaptive Choropleth Mapper: Scatter Plot with Correlation",
        'filename_suffix': "Adaptive_Choropleth_Maps_Scatter",
        'state_fips': None,    #fips code is avaiable for every US state at https://github.com/suhanmappingideas/geosnap-viz/blob/master/state_id.csv
        'msa_fips': "16980",   #For more options: http://su-gis.iptime.org/LNE/pick_POI.html
        'county_fips': None,   #county code 
        'year': 2000, #Available years: 1970, 1980, 1990, 2000, 2010
        'variables': [
            "p_nonhisp_white_persons",
            "p_nonhisp_black_persons",
            "p_hispanic_persons",
            "p_native_persons",
            "p_asian_persons",
            "p_hawaiian_persons",
            "p_asian_indian_persons",
            "p_chinese_persons",
            "p_filipino_persons",
            "p_japanese_persons",
            "p_korean_persons",
            "p_vietnamese_persons",
            "median_household_income",
            "median_income_whitehh",
            "median_income_blackhh",
            "median_income_hispanichh",
            "median_income_asianhh",
            "per_capita_income",
        ],
        'chart': "Scatter Plot",
        'label': "short_name",                                       # variable, short_name or full_name
}
Adaptive_Choropleth_Mapper_viz(param)<jupyter_output><empty_output><jupyter_text>4. Adaptive Choropleth Mapper with Parallel Coordinate Plot<jupyter_code>param = {
        'title': "Adaptive Choropleth Mapper with PCP",
        'filename_suffix': "Adaptive_Choropleth_Maps_Parallel_Coordinate_Plot",
        'state_fips': None,    #fips code is avaiable for every US state at https://github.com/suhanmappingideas/geosnap-viz/blob/master/state_id.csv
        'msa_fips': "16980",   #For more options: http://su-gis.iptime.org/LNE/pick_POI.html
        'county_fips': None,   #county code 
        'year': 2000, #Available years: 1970, 1980, 1990, 2000, 2010
        'NumOfMaps': 6,
        'label': "short_name", #Pick variable,short_name,full_name from template/conversion_table_codebook.csv         
        'variables': [         #enter variable names of the column you selected above.
            "p_nonhisp_white_persons",
            "p_nonhisp_black_persons",
            "p_hispanic_persons",
            "p_asian_persons",           
            "p_persons_over_60",
            "p_foreign_born_pop",
            "p_edu_college_greater",
            "p_poverty_rate",              
            "p_unemployment_rate",
            "p_employed_manufacturing",
            "p_persons_under_18",
            "p_vacant_housing_units",
            "p_owner_occupied_units",
            "p_housing_units_multiunit_structures",
            "median_home_value",
            "p_structures_30_old",
            "p_household_recent_move",
 
        ],
        'chart': "Parallel Coordinates Plot",

}
Adaptive_Choropleth_Mapper_viz(param)<jupyter_output><empty_output><jupyter_text>5. Adaptive Choropleth Mapper with Time Series<jupyter_code>param = {
        'title': "Adaptive Choropleth Mapper: Time Series",
        'filename_suffix': "Adaptive_Choropleth_Maps_Time_Series",
        'state_fips': None,    #fips code is avaiable for every US state at https://github.com/suhanmappingideas/geosnap-viz/blob/master/state_id.csv
        'msa_fips': "16980",   #For more options: http://su-gis.iptime.org/LNE/pick_POI.html
        'county_fips': None,   #county code 
        'years': [1980, 1990, 2000, 2010], #Available years: 1970, 1980, 1990, 2000, 2010
        'NumOfMaps': 4,
        'label': "short_name", #Pick variable,short_name,full_name from template/conversion_table_codebook.csv    
        'variables': [
            "p_hispanic_persons",        
        ],    
        'chart': "Parallel Coordinates Plot",
}
Adaptive_Choropleth_Mapper_viz(param)<jupyter_output><empty_output>
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