diff --git a/dashboard_code/dashboard.py b/dashboard_code/dashboard.py
index aa39baa1e7b8388e53639c3efb9c50330a724d0f..1014dec2b9099255dc43984723957aea02e51c79 100644
--- a/dashboard_code/dashboard.py
+++ b/dashboard_code/dashboard.py
@@ -61,7 +61,7 @@ class Dashboard:
margin-top: 20px;
}}
iframe {{
- height: 800px; /* Set a fixed height for the iframe */
+ height: 1000px; /* Increase the height for better legibility */
width: 100%;
}}
</style>
diff --git a/data_code/querys.py b/data_code/querys.py
index db4791569249812c4c5c598d5c2b3873fbf0624b..0d936ceb74609e315e9c8cdf4aadef894297ded2 100644
--- a/data_code/querys.py
+++ b/data_code/querys.py
@@ -99,11 +99,9 @@ def Government_education_spending():
def uk_education_expenditure():
- # Use COALESCE to select the non-NULL value between 't_expenditure_millions' and 't_expenditure'
education_expenditure = """
- SELECT time_period, t_expenditure_millions
- FROM data
- WHERE education_function = 'Total' AND expenditure_type = 'Total' AND expenditure_level = 'Total';
+ SELECT time_period,time_identifier,geographic_level,country_code,country_name,education_function,expenditure_type,expenditure_level,t_expenditure_millions
+ FROM data;
"""
return education_expenditure
@@ -116,7 +114,7 @@ def school_funding_uk_county():
def UK_crime_data():
crime_data = """
- SELECT Year, Area_Code, "Total recorded crime (excluding fraud) [note 2]"
+ SELECT Year, Area_Code, Area_Name, "Total recorded crime (excluding fraud) [note 2]","Violence against the person","Homicide","Violence with injury","Violence without injury","Sexual offences","Robbery","Theft offences","Burglary","Domestic burglary","Non-domestic burglary","Vehicle offences","Theft from the person","Bicycle theft","Shoplifting","All other theft offences","Criminal damage and arson","Drug offences","Possession of weapon offences","Public order offences","Miscellaneous crimes against society","Death or serious injury - unlawful driving","Stalking and harassment","Residential burglary","Non-residential burglary","Other theft offences","Possession of weapons offences","Miscellaneous crimes"
FROM data;
"""
return crime_data
diff --git a/data_storage/UK/Crime/combined_crime_uk.db b/data_storage/UK/Crime/combined_crime_uk.db
index 0615d7721e562c20efb042a9dafbe04158ec42b4..1fa931e47c5be68062228d24ea0bb16fa9906c24 100644
Binary files a/data_storage/UK/Crime/combined_crime_uk.db and b/data_storage/UK/Crime/combined_crime_uk.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_expenditure.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_expenditure.db
index 7001c7828c24da8f7ee7370e506f3a9a6a893a59..401c2b9032375f9b3bcdeee5e7d1a26ae4705cf4 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_expenditure.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_expenditure.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_highest_qualifications_time_series_rounded.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_highest_qualifications_time_series_rounded.db
index 111c7b6ab9e611127107942f5da22f1338d115ad..c3fd6a9aeb651b4d8cfe4b9126377138f5ad3c3b 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_highest_qualifications_time_series_rounded.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_highest_qualifications_time_series_rounded.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_neet.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_neet.db
index 29040517378d9ca9d38caab1d00fe330f49e89cf..7f616ede93ab26edd2635b957fa0677edfca5a5c 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_neet.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_neet.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_fe_students.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_fe_students.db
index b64775fbe80acba757ed6625c49738c339627dfd..4be305e4cf25173865811f5d3d082666ef23e183 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_fe_students.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_fe_students.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_he_students.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_he_students.db
index 078b61a92066709d80b6f76acb31fc22826f55c7..da2f3f18a6e2d38b8c6df98fe50cb6057456a554 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_he_students.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_he_students.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_institutions.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_institutions.db
index 263adac80813f0b930df99c0dc27cd62e815a414..99816aaac3e7cd7c9754e0786e91eaee0416167d 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_institutions.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_post_compulsory_education_institutions.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupil_teacher_ratios.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupil_teacher_ratios.db
index 9eafbe81c581327a45ec3e2e1fb07329a6ad0fec..20a1e7fffbdd98c07469fbb8d57e3fcbc4e7ee04 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupil_teacher_ratios.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupil_teacher_ratios.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupils_suppressed.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupils_suppressed.db
index 0e451dc5b496e04d9dd673e6a12b6bfeccb62e72..6c8a6c544737a3043a8a9e2e2cca11a636829751 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupils_suppressed.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_pupils_suppressed.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_schools.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_schools.db
index 49d9c0b800ccb5d8c101e288b35af604f57382c9..6bef7ae78dc80444a04dd1dedf1a808ce465d285 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_schools.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_schools.db differ
diff --git a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_teachers_suppressed.db b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_teachers_suppressed.db
index 5c30189713d501fdd6516bc29e052501e329778e..83ebb2ce70d2f061e0271da1fb40d5538d2ee77e 100644
Binary files a/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_teachers_suppressed.db and b/data_storage/UK/Education/education-and-training-statistics-for-the-uk_2024/data/uk_teachers_suppressed.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_la_regional_national.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_la_regional_national.db
index bcfaaf97444048555412d7882ceead7686f76beb..ba706a9a89788bfd3d386f684f73cd1d84fa8fa2 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_la_regional_national.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_la_regional_national.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_national_summary.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_national_summary.db
index 61ff3416b18a5c5cb313c6ba7221ce0c86bf9020..6addce62c01f6d2559a9a2cda2e6d20c54447b59 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_national_summary.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_income_national_summary.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserve_la_regional_national_data.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserve_la_regional_national_data.db
index db728a6e6deb9d84cd1af2adc30641234fd34976..86e4bb347fccf46b0a9270eb565bc62f7947bbfb 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserve_la_regional_national_data.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserve_la_regional_national_data.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserves_national_rounded_data.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserves_national_rounded_data.db
index a9193ff2d8b1addd2330601fac6f1b74ad6decc9..8ff5b8304650dc242439b59bcaa85752ee838684 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserves_national_rounded_data.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/cfr_revenue_reserves_national_rounded_data.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/headline_key_figures.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/headline_key_figures.db
index e92e9be3e15bec0105952b996013e3ba42978b31..320ad3e621648f7d1280c7bcfc36f738b1deebc3 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/headline_key_figures.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/headline_key_figures.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_la_regional_national.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_la_regional_national.db
index 3835a0dd115c55ec79bc2de61a34cc8cff2a93d2..9237dd9f835178017225ef0c5c6a82e87151fa38 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_la_regional_national.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_la_regional_national.db differ
diff --git a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_national.db b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_national.db
index 051f02b40887f5b69decae3ce68d4338a76af4a8..319f300bd8e4ec72d9898bf8e1f1ea180c5f7422 100644
Binary files a/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_national.db and b/data_storage/UK/Education/la-and-school-expenditure_2023-24/data/s251_childrens_young_peoples_services_national.db differ
diff --git a/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_fig2.db b/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_fig2.db
index 1f7a70264693ff20f838c788d182484dbc2669fd..5b6509a28e58b2dba3efe95b1878a2e26ba2cf06 100644
Binary files a/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_fig2.db and b/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_fig2.db differ
diff --git a/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_school_level_data_csv.db b/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_school_level_data_csv.db
index 4d769a65f4a4f2bfb987a7e68c2dc6af98991f81..2ca3897c68d908e382e129c5e26230e39a4d22db 100644
Binary files a/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_school_level_data_csv.db and b/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_school_level_data_csv.db differ
diff --git a/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_tab1.db b/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_tab1.db
index 726d3dc00454eec4591610e66852c080a15cc73f..98ab82ed834f8c36b3e69833c28639e96e8ba6b8 100644
Binary files a/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_tab1.db and b/data_storage/UK/Education/school-funding-statistics_2023-24/data/20240125_tab1.db differ
diff --git a/data_storage/UN/Crime/SYB67_328_202411_Intentional homicides and other crimes.db b/data_storage/UN/Crime/SYB67_328_202411_Intentional homicides and other crimes.db
index 369406d766e7136894581bf3c58c65be650d089d..62acbcaa03cdc4369423e94b4ad0333bc0ffe18c 100644
Binary files a/data_storage/UN/Crime/SYB67_328_202411_Intentional homicides and other crimes.db and b/data_storage/UN/Crime/SYB67_328_202411_Intentional homicides and other crimes.db differ
diff --git a/data_storage/UN/Economy/Government_final_consumption_expenditure_by_function_at_current_prices.db b/data_storage/UN/Economy/Government_final_consumption_expenditure_by_function_at_current_prices.db
index e526d48ab28d8bc1b84b84d2ea545adc13f58973..f2d38ae15b2ea0522cf6d2fae5403c243c77f2b5 100644
Binary files a/data_storage/UN/Economy/Government_final_consumption_expenditure_by_function_at_current_prices.db and b/data_storage/UN/Economy/Government_final_consumption_expenditure_by_function_at_current_prices.db differ
diff --git a/data_storage/UN/Economy/SYB67_128_202411_Consumer Price Index.db b/data_storage/UN/Economy/SYB67_128_202411_Consumer Price Index.db
index ee7474727d98c79900e5461ad6c216a487272fe6..9dede1162fbcf535175e3830c9477428cfa22c6b 100644
Binary files a/data_storage/UN/Economy/SYB67_128_202411_Consumer Price Index.db and b/data_storage/UN/Economy/SYB67_128_202411_Consumer Price Index.db differ
diff --git a/data_storage/UN/Economy/SYB67_230_202411_GDP and GDP Per Capita.db b/data_storage/UN/Economy/SYB67_230_202411_GDP and GDP Per Capita.db
index e4a71b9c9fd670988f3eafcaedea0891e652cc03..b5881ceecc6fa1f395d3b30543c2b6e3522a3920 100644
Binary files a/data_storage/UN/Economy/SYB67_230_202411_GDP and GDP Per Capita.db and b/data_storage/UN/Economy/SYB67_230_202411_GDP and GDP Per Capita.db differ
diff --git a/data_storage/UN/Education/SYB67_245_202411_Public expenditure on education and access to computers.db b/data_storage/UN/Education/SYB67_245_202411_Public expenditure on education and access to computers.db
index 4d62e02dffc33fa06cd307933b75969afb655765..cf517ea1e4f6a97222db59608d2b032380d9d1ca 100644
Binary files a/data_storage/UN/Education/SYB67_245_202411_Public expenditure on education and access to computers.db and b/data_storage/UN/Education/SYB67_245_202411_Public expenditure on education and access to computers.db differ
diff --git a/data_storage/UN/Education/SYB67_309_202411_Education.db b/data_storage/UN/Education/SYB67_309_202411_Education.db
index 6d62f3e4f23da7d616f4cfb4870445920c97fff2..ecdd1175f9badca82aae2e93a69759943bc93695 100644
Binary files a/data_storage/UN/Education/SYB67_309_202411_Education.db and b/data_storage/UN/Education/SYB67_309_202411_Education.db differ
diff --git a/data_storage/UN/Education/SYB67_323_202411_Teaching Staff in education.db b/data_storage/UN/Education/SYB67_323_202411_Teaching Staff in education.db
index dab483bbfa03b8937d7c9cc32f1697df3ce56cec..5dad2fc5f996cc85137162892b124e05dbd51912 100644
Binary files a/data_storage/UN/Education/SYB67_323_202411_Teaching Staff in education.db and b/data_storage/UN/Education/SYB67_323_202411_Teaching Staff in education.db differ
diff --git a/data_storage/UN/Gender/SYB67_319_202411_Ratio of girls to boys in education.db b/data_storage/UN/Gender/SYB67_319_202411_Ratio of girls to boys in education.db
index c3b395b4759559fe758ddef21d0a40252288636f..dedea0417b55292f91c8992b5d30f4465f5b0de3 100644
Binary files a/data_storage/UN/Gender/SYB67_319_202411_Ratio of girls to boys in education.db and b/data_storage/UN/Gender/SYB67_319_202411_Ratio of girls to boys in education.db differ
diff --git a/data_storage/UN/Health/SYB67_154_202411_Health Personnel.db b/data_storage/UN/Health/SYB67_154_202411_Health Personnel.db
index c3dbd60fdd70b14960fc1254be00a7d5dcd6f424..ffdfbcabbb0e282d6d42903dc4533dd16a9675ea 100644
Binary files a/data_storage/UN/Health/SYB67_154_202411_Health Personnel.db and b/data_storage/UN/Health/SYB67_154_202411_Health Personnel.db differ
diff --git a/data_storage/UN/Health/SYB67_325_202411_Expenditure on health.db b/data_storage/UN/Health/SYB67_325_202411_Expenditure on health.db
index 89f37d7a4cd3ad3349984bf2c1131c6efdd92997..b0623d80acf8e2c2971df9dcb0ccdae94fed0fab 100644
Binary files a/data_storage/UN/Health/SYB67_325_202411_Expenditure on health.db and b/data_storage/UN/Health/SYB67_325_202411_Expenditure on health.db differ
diff --git a/data_storage/UN/Internet/SYB67_314_202411_Internet Usage.db b/data_storage/UN/Internet/SYB67_314_202411_Internet Usage.db
index 9b5e6ddc08326dc8ddbc2f6003486fd79ae077ea..6d4fd3d6e6f951dc1eb4ed759a94f75558a495ab 100644
Binary files a/data_storage/UN/Internet/SYB67_314_202411_Internet Usage.db and b/data_storage/UN/Internet/SYB67_314_202411_Internet Usage.db differ
diff --git a/data_storage/UN/Labour/SYB67_200_202411_Employment.db b/data_storage/UN/Labour/SYB67_200_202411_Employment.db
index ec7c161ea6c2c0763268029bf35bff041b2b923d..a62a431708dcf8ecdc7454587d79cddae14b8316 100644
Binary files a/data_storage/UN/Labour/SYB67_200_202411_Employment.db and b/data_storage/UN/Labour/SYB67_200_202411_Employment.db differ
diff --git a/data_storage/UN/Labour/SYB67_329_202411_Labour Force and Unemployment.db b/data_storage/UN/Labour/SYB67_329_202411_Labour Force and Unemployment.db
index d3fda4ef93ba8794f72b46efa62bf5da451bcd84..28fe87461d33adddf9757e9d6afc4fc39d23bae3 100644
Binary files a/data_storage/UN/Labour/SYB67_329_202411_Labour Force and Unemployment.db and b/data_storage/UN/Labour/SYB67_329_202411_Labour Force and Unemployment.db differ
diff --git a/data_storage/UN/Land/SYB67_145_202411_Land.db b/data_storage/UN/Land/SYB67_145_202411_Land.db
index 2decbc93c86bbc0c1ad163be0a67a8eebc770d1e..72782573fe79abc40bf536b632d103d63b0fd2c5 100644
Binary files a/data_storage/UN/Land/SYB67_145_202411_Land.db and b/data_storage/UN/Land/SYB67_145_202411_Land.db differ
diff --git a/graphs/graphs_display.html b/graphs/graphs_display.html
index f90c0533bec1a30317c061cbc3e8ba9bb99ab195..534b003b085422104ea35cd598b56daa0b7fc06f 100644
Binary files a/graphs/graphs_display.html and b/graphs/graphs_display.html differ
diff --git a/main.py b/main.py
index 4c846809688e6e896279c508b46c3cd734a7c709..040d1ba45abd4a48aa862eff69eee9bf388d6c1d 100644
--- a/main.py
+++ b/main.py
@@ -10,6 +10,8 @@ import warnings
import webbrowser
import os
import statsmodels.api as sm
+import plotly.graph_objects as go
+
# Ignore deprecation warnings
@@ -30,7 +32,6 @@ table_list_UK = sql_t_UK.list_db(verbose=False)
def combined_graph():
- import plotly.graph_objects as go
male_homicide_rate_query = querys.male_homicide_rate()
@@ -320,126 +321,272 @@ def combined_choropleth():
def graph8():
-
-
- # Load the GeoJSON file
- gpkg_path = "UK_Choropleth/Counties_and_Unitary_Authorities_May_2023_UK_BGC_-5421515367278074247.gpkg"
- counties = gpd.read_file(gpkg_path)
- #print(counties.head())
-
- # Reproject to WGS 84 (EPSG:4326)
- counties = counties.to_crs(epsg=4326)
-
- # Convert GeoDataFrame to GeoJSON
- counties_json = json.loads(counties.to_json())
-
- # Create a DataFrame with the necessary data
- data = {
- "CTYUA23CD": [feature["properties"]["CTYUA23CD"] for feature in counties_json["features"]],
- "CTYUA23NM": [feature["properties"]["CTYUA23NM"] for feature in counties_json["features"]]
- }
- data = pd.DataFrame(data)
-
- # Join school funding on CTYUA23CD and la_name
- school_funding_query = querys.school_funding_uk_county()
- school_funding = sql_t_UK.query_db(school_funding_query, table_name="20240125_school_level_data_csv.db")
- school_funding = school_funding.rename(columns={"new_la_code": "CTYUA23CD"})
-
- school_funding = school_funding.groupby("CTYUA23CD", as_index=False)["total_funding"].mean()
-
- # Merge and keep all columns from the original data and add total_funding
- data = data.merge(school_funding[['CTYUA23CD', 'total_funding']], on="CTYUA23CD", how="left")
-
- #print(data.head())
-
- # Create Plotly choropleth map
- fig = px.choropleth_mapbox(
- data,
- geojson=counties_json,
- locations="CTYUA23CD",
- featureidkey="properties.CTYUA23CD",
- color="total_funding", # Change this to the column you want to color by
- hover_name="CTYUA23NM", # Column to display on hover
- hover_data=["CTYUA23CD", "total_funding"], # Additional data to display on hover
- mapbox_style="carto-positron", # Use a less detailed mapbox style
- title="Average Funding of Schools Per Local Area Code", # Title of the map
- labels={"total_funding": "Average Funding Per School"}, # Label for the color scale
- zoom=5,
- center={"lat": 54.0, "lon": -2.0},
- opacity=1
+ # Query the education expenditure data
+ education_expenditure_query = querys.uk_education_expenditure()
+ education_expenditure = sql_t_UK.query_db(education_expenditure_query, table_name="uk_expenditure.db")
+
+ # Convert time_period to a standard year format
+ education_expenditure["time_period"] = education_expenditure["time_period"].astype(str).str[:4]
+ education_expenditure["time_period"] = pd.to_numeric(education_expenditure["time_period"], errors="coerce")
+
+ # Filter only 'Total' expenditure type to avoid double-counting
+ education_expenditure = education_expenditure[education_expenditure["expenditure_type"] == "Total"]
+
+ # Ensure the data is sorted by time_period
+ education_expenditure = education_expenditure.sort_values(by="time_period")
+
+ # Remove non-numeric or missing values in t_expenditure_millions
+ education_expenditure["t_expenditure_millions"] = pd.to_numeric(education_expenditure["t_expenditure_millions"], errors="coerce")
+ education_expenditure = education_expenditure.dropna(subset=["t_expenditure_millions"])
+
+ # Group by time_period and education_function to get the total expenditure
+ grouped_df = education_expenditure.groupby(["time_period", "education_function"], as_index=False)["t_expenditure_millions"].sum()
+
+ # Create a stacked bar chart by year
+ fig = px.bar(
+ grouped_df,
+ x="time_period",
+ y="t_expenditure_millions",
+ color="education_function",
+ barmode="stack",
+ labels={"time_period": "Year", "t_expenditure_millions": "Expenditure (Millions)"},
+ title="UK Education Spending"
+ )
+
+
+ # Update the y-axis to have regular intervals
+ fig.update_layout(
+ yaxis=dict(
+ tickmode='linear',
+ tick0=0,
+ dtick=100000 # Set the interval for y-axis ticks
+ ),
+ xaxis_title="Financial Year",
+ yaxis_title="Total Expenditure (Millions)"
)
-
- # # Show plot
return fig
-
-
-def graph9():
-
-
- # Load the GeoJSON file
+def graph9(): # CRIME MAP
+ # Load GeoDataFrame
gpkg_path = "UK_Choropleth/Police_Force_Areas_December_2022_EW_BFE_4519315402050042056.geojson"
- counties = gpd.read_file(gpkg_path)
- #print(counties.head())
- # Reproject to WGS 84 (EPSG:4326)
- counties = counties.to_crs(epsg=4326)
-
- # Convert GeoDataFrame to GeoJSON
+ counties = gpd.read_file(gpkg_path).to_crs(epsg=4326)
pfa_json = json.loads(counties.to_json())
- # Create a DataFrame with the necessary data
- data = {
- "PFA22CD": [feature["properties"]["PFA22CD"] for feature in pfa_json["features"]],
- "PFA22NM": [feature["properties"]["PFA22NM"] for feature in pfa_json["features"]]
- }
- data = pd.DataFrame(data)
-
- # Get crime statistics
+ data = pd.DataFrame({
+ "PFA22CD": [feat["properties"]["PFA22CD"] for feat in pfa_json["features"]],
+ "PFA22NM": [feat["properties"]["PFA22NM"] for feat in pfa_json["features"]]
+ })
+
+ # Query + rename
crime_data_query = querys.UK_crime_data()
crime_data = sql_t_UK.query_db(crime_data_query, table_name="combined_crime_uk.db")
crime_data = crime_data.rename(columns={"Area_Code": "PFA22CD"})
-
- # Clean up my crime_data before grouping
- crime_data["Total recorded crime (excluding fraud) [note 2]"] = pd.to_numeric(
- crime_data["Total recorded crime (excluding fraud) [note 2]"],
- errors="coerce"
+
+ # Use your full list of columns
+
+ crime_columns = [
+ "Total recorded crime (excluding fraud) [note 2]",
+ "Violence against the person", "Homicide", "Violence with injury",
+ "Violence without injury", "Sexual offences", "Robbery", "Theft offences",
+ "Burglary", "Domestic burglary", "Non-domestic burglary", "Vehicle offences",
+ "Theft from the person", "Bicycle theft", "Shoplifting", "All other theft offences",
+ "Criminal damage and arson", "Drug offences", "Possession of weapon offences",
+ "Public order offences", "Miscellaneous crimes against society",
+ "Death or serious injury - unlawful driving", "Stalking and harassment",
+ "Residential burglary", "Non-residential burglary", "Other theft offences",
+ "Possession of weapons offences", "Miscellaneous crimes"
+ ]
+
+ # Convert columns to numeric, group + average
+ for col in crime_columns:
+ if col in crime_data.columns:
+ crime_data[col] = pd.to_numeric(crime_data[col], errors="coerce")
+ grouped_df = crime_data.groupby("PFA22CD", as_index=False)[crime_columns].mean()
+
+ # Merge
+ data = data.merge(grouped_df, on="PFA22CD", how="left")
+
+ # Create one trace initially (e.g. default to your first column)
+ default_col = crime_columns[0]
+ fig = go.Figure(
+ go.Choroplethmapbox(
+ geojson=pfa_json,
+ locations=data["PFA22NM"],
+ featureidkey="properties.PFA22NM",
+ z=data[default_col],
+ colorscale="Reds",
+ zmin=0,
+ zmax=data[default_col].max(skipna=True) or 0,
+ marker_line_width=0.5,
+ marker_line_color="gray",
+ hovertemplate="Police Force: %{location}<br>" + default_col + ": %{z}"
+ )
)
- # Now group and average
- crime_data = crime_data.groupby("PFA22CD", as_index=False)[
- "Total recorded crime (excluding fraud) [note 2]"
- ].mean()
+ # Build update menu with each column
+ buttons = []
+ for col in crime_columns:
+ buttons.append(
+ dict(
+ label=col,
+ method="restyle",
+ args=[
+ {"z": [data[col].fillna(0)], # new z array
+ "hovertemplate": [f"Police Force: %{{location}}<br>{col}: %{{z}}"],
+ "zmax": [float(data[col].max(skipna=True) or 0)]
+ }
+ ],
+ )
+ )
-
- data = data.merge(crime_data[['PFA22CD', 'Total recorded crime (excluding fraud) [note 2]']], on="PFA22CD", how="left")
+ fig.update_layout(
+ mapbox_style="carto-positron",
+ mapbox_zoom=5,
+ mapbox_center={"lat": 54.0, "lon": -2.0},
+ margin={"r": 0, "t": 50, "l": 0, "b": 0},
+ updatemenus=[
+ dict(
+ buttons=buttons,
+ direction="down",
+ showactive=True,
+ x=0.02,
+ xanchor="left",
+ y=0.98,
+ yanchor="top"
+ )
+ ],
+ title=f"UK Crime Map: {default_col}"
+ )
+ return fig
-
+def graph10():
+ # Query the crime data
+ crime_data_query = querys.UK_crime_data()
+ crime_data = sql_t_UK.query_db(crime_data_query, table_name="combined_crime_uk.db")
- # Create Plotly choropleth map
- fig = px.choropleth_mapbox(
- data,
- geojson=pfa_json,
- locations="PFA22NM",
- featureidkey="properties.PFA22NM",
- color="Total recorded crime (excluding fraud) [note 2]", # Change this to the column you want to color by
- hover_name="PFA22NM", # Column to display on hover
- hover_data=["PFA22NM", "Total recorded crime (excluding fraud) [note 2]"], # Additional data to display on hover
- mapbox_style="carto-positron", # Use a less detailed mapbox style
- title="Total Recorded Crime", # Title of the map
- labels={"Total recorded crime (excluding fraud) [note 2]": "Total Recorded Crime"}, # Label for the color scale
- zoom=5,
- center={"lat": 54.0, "lon": -2.0},
- opacity=1
+ # List of crime columns
+ crime_columns = [
+ "Total recorded crime (excluding fraud) [note 2]",
+ "Violence against the person", "Homicide", "Violence with injury",
+ "Violence without injury", "Sexual offences", "Robbery", "Theft offences",
+ "Burglary", "Domestic burglary", "Non-domestic burglary", "Vehicle offences",
+ "Theft from the person", "Bicycle theft", "Shoplifting", "All other theft offences",
+ "Criminal damage and arson", "Drug offences", "Possession of weapon offences",
+ "Public order offences", "Miscellaneous crimes against society",
+ "Death or serious injury - unlawful driving", "Stalking and harassment",
+ "Residential burglary", "Non-residential burglary", "Other theft offences",
+ "Possession of weapons offences", "Miscellaneous crimes"
+ ]
+
+ # Convert columns to numeric
+ for col in crime_columns:
+ if col in crime_data.columns:
+ crime_data[col] = pd.to_numeric(crime_data[col], errors="coerce")
+
+ # Ensure the data is sorted by Year
+ crime_data = crime_data.sort_values(by="Year")
+
+ # Remove non-numeric or missing values in Year
+ crime_data["Year"] = pd.to_numeric(crime_data["Year"], errors="coerce")
+ crime_data = crime_data.dropna(subset=["Year"])
+
+ # Group by year
+ grouped_df = crime_data.groupby(["Year"], as_index=False)["Total recorded crime (excluding fraud) [note 2]"].sum()
+ # Create a line chart by year
+ fig = px.line(
+ grouped_df,
+ x="Year",
+ y="Total recorded crime (excluding fraud) [note 2]",
+ labels={"Year": "Year", "Total recorded crime (excluding fraud) [note 2]": "Total Crime"},
+ title="Total Recorded Crime Over Time by Area"
)
+ return fig
+
+def graph11():
+ # Query the crime data
+ crime_data_query = querys.UK_crime_data()
+ crime_data = sql_t_UK.query_db(crime_data_query, table_name="combined_crime_uk.db")
- # # Show plot
- return fig
+ # List of crime columns (excluding the total recorded crime column)
+ crime_columns = [
+ "Violence against the person", "Homicide", "Violence with injury",
+ "Violence without injury", "Sexual offences", "Robbery", "Theft offences",
+ "Burglary", "Domestic burglary", "Non-domestic burglary", "Vehicle offences",
+ "Theft from the person", "Bicycle theft", "Shoplifting", "All other theft offences",
+ "Criminal damage and arson", "Drug offences", "Possession of weapon offences",
+ "Public order offences", "Miscellaneous crimes against society",
+ "Death or serious injury - unlawful driving", "Stalking and harassment",
+ "Residential burglary", "Non-residential burglary", "Other theft offences",
+ "Possession of weapons offences", "Miscellaneous crimes"
+ ]
+ # Convert columns to numeric
+ for col in crime_columns:
+ if col in crime_data.columns:
+ crime_data[col] = pd.to_numeric(crime_data[col], errors="coerce")
+
+ # Ensure the data is sorted by Year
+ crime_data = crime_data.sort_values(by="Year")
+
+ # Remove non-numeric or missing values in Year
+ crime_data["Year"] = pd.to_numeric(crime_data["Year"], errors="coerce")
+ crime_data = crime_data.dropna(subset=["Year"])
+
+ # Create frames for the animation
+ frames = []
+ for year in crime_data["Year"].unique():
+ year_data = crime_data[crime_data["Year"] == year]
+ year_data = year_data[crime_columns].sum().reset_index()
+ year_data.columns = ["Crime Type", "Count"]
+
+ frames.append(go.Frame(
+ data=[go.Pie(labels=year_data["Crime Type"], values=year_data["Count"], pull=[0]*len(year_data), textinfo="none")],
+ name=str(year)
+ ))
+
+ # Create the initial pie chart
+ initial_year = crime_data["Year"].unique()[0]
+ initial_data = crime_data[crime_data["Year"] == initial_year]
+ initial_data = initial_data[crime_columns].sum().reset_index()
+ initial_data.columns = ["Crime Type", "Count"]
+
+ fig = go.Figure(
+ data=[go.Pie(labels=initial_data["Crime Type"], values=initial_data["Count"], pull=[0]*len(initial_data), textinfo="none")],
+ layout=go.Layout(
+ title=f"Crime Distribution for {initial_year}",
+ updatemenus=[dict(
+ type="buttons",
+ showactive=False,
+ buttons=[dict(label="Play",
+ method="animate",
+ args=[None, {"frame": {"duration": 1000, "redraw": True}, "fromcurrent": True}]),
+ dict(label="Pause",
+ method="animate",
+ args=[[None], {"frame": {"duration": 0, "redraw": False}, "mode": "immediate"}])]
+ )],
+ sliders=[{
+ "steps": [
+ {"args": [[str(year)], {"frame": {"duration": 1000, "redraw": True}, "mode": "immediate"}],
+ "label": str(year),
+ "method": "animate"} for year in crime_data["Year"].unique()
+ ],
+ "transition": {"duration": 300},
+ "x": 0.1,
+ "xanchor": "left",
+ "y": 0,
+ "yanchor": "top"
+ }]
+ ),
+ frames=frames
+ )
+
+ return fig
+
@@ -462,8 +609,10 @@ if __name__ == "__main__":
graphs = [
("Combined Choropleth", combined_choropleth, "UN"),
("Combined Test", combined_graph, "UN"),
- ("Average Funding of Schools Per Local Area Code", graph8, "UK"),
- ("Total Recorded Crime", graph9, "UK"),
+ ("Total Recorded Crime 2015-2024", graph9, "UK"),
+ ("UK Education Expendature", graph8, "UK"),
+ ("UK Crime Bar Chart", graph10, "UK"),
+ ("Distribution of Crime Types", graph11, "UK")
]
# Add each graph to the dashboard with a progress bar