Census 1850 is a data layer within the U.S. Decennial Census category of the Social Explorer application. To find each layer, log in to Social Explorer, select a category, and browse to the desired layer.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Solana Social Explorer on 2025-07-23. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

U.S. Congressional Elections 2010 is a data layer within the U.S. Election Data category of the Social Explorer application. To find each layer, log in to Social Explorer, select a category, and browse to the desired layer.

The Equity Explorer Tool allows users to explore census tracts throughout Los Angeles County to identify areas of the highest need based on populations disproportionately affected by COVID-19 prior to embarking on project design by either using the map or a series of filters.To use the Equity Explorer, users can leverage the following capabilities:Core COVID Filters: Apply the various COVID filters in the Core COVID Filters section of the far left pane. These filters include the COVID index scores and categories, the individual index components, HUD Qualified tract status, and other location attributes (like CSA). As filters in this section are applied, the map will update to reflect only tracts meeting the criteria and the summary statistics and table will update accordingly. To turn the filter on, toggle the radio button to the right of the filter. The filter is on when the button is blue. Thematic Filters: Apply any additional filters in the Thematic Filters section. Please note, these filters do not impact the summary statistics at the bottom of the application or the table of tracts. The corresponding layer(s) will need to be turned on using the map layer list to see the filter results. Map Selection: In addition to the above filters, tracts can also be selected directly on the map using the map select tool in the upper left corner of the map. Table Widget: Once the list of tracts has been narrowed down appropriately for the program, tracts can be exported by clicking the table widget in the upper right corner, next to the documentation button. Navigate to the COVID Index tab, click the 4 dot icon to the right of the table, and export records as a CSV. Summary Statistics: As the COVID filters are applied or a selection is made on the map, the statistics at the bottom of the screen will update. Map Layer List: To additional layers on or off the map, click the eye icon next to a layer name in the map layer list in the far right paneMap Legend: The map legend in the bottom right corner will update to show information about the layers currently being visualized on the map.For more information, please contact egis@isd.lacounty.gov or race-equity@ceo.lacounty.gov

About the App This app hosts data from Heat Resilience Solutions for Boston (the Heat Plan). It features maps that include daytime and nighttime air temperature, urban heat island index, and extreme heat duration. About the DataA citywide urban canopy model was developed to produce modeled air temperature maps for the City of Boston Heat Resilience Study in 2021. Sasaki Associates served as the lead consultant working with the City of Boston. The technical methodology for the urban canopy model was produced by Klimaat Consulting & Innovation Inc. A weeklong analysis period during July 18th-24th, 2019 was selected to produce heat characteristics maps for the study (one of the hottest weeks in Boston that year). The data array represents the modelled, average hourly urban meteorological condition at 100 meter spatial resolution. This dataset was processed into urban heat indices and delivered as georeferenced image layers. The data layers have been resampled to 10 meter resolution for visualization purposes. For the detailed methodology of the urban canopy model, visit the Heat Resilience Study project website.

The social assistance explorer contains a harmonised panel dataset of social assistance indicators spanning 2000-2015. It has been developed to support comparative research on emerging welfare institutions. Comparative analysis of social protection institutions in low and middle income countries is scarce. Yet Social Assistance accounts for most of the recent expansion of welfare institutions. Led by Professor Armando Barrientos, the two-year research project was based at the Global Development Institute at The University of Manchester and was funded by the ESRC. It drew on the knowledge and expertise of a large number of collaborators across the world. The project collected data on programme design and objectives, institutionalisation, reach, and financial resources. Key indicators can be aggregated at country and region levels.

These are the zip codes in the catchment area (Zipcodes sheet) and the zip codes from Social Explorer that overlap with the zip codes in the catchment area (ACSdata sheet).

This dissertation project identifies the anti-colonial and anti-racist traditions that Black and Brown Angelenos have created, specifically the artworks expressing cultural pride and solidarity with each other. While other scholars have looked at Black and Latina/o/x Los Angeles together, few have looked at the trends and traditions within visual culture and art history. This particular intervention is historical, but also builds from the contemporary moment we live in, where underpaid school teachers have been striking en masse, where women are proclaiming #TimesUp, where Black Lives Matter is ushering perhaps the largest social movement in U.S. history, and still, the movement continues to grow all over the world. Furthermore, this dissertation has been informed by the COVID-19 crisis, which deeply and disproportionately impacts housing, employment, health outcomes and many other factors for people of color, especially Native Peoples, African Americans and Latinx folks in the U.S. As a way to reframe this political moment of pandemics, social injustice, and consciousness raising, I freedom dream through Afro-Latinx Futurism, a concept I offer that empowers Black, Latinx and Afro-Latinx people to center pleasure, rest, and joy as a visual practice in the arts and an important expression of liberation. Together, this project will forge a new history of the past by offering analysis of artworks, but also, moments when people lived, fought and created together. In some cases, I will highlight works of art that were not exactly made together, or directly in conversation with the other, but still work within a constellation of struggle against US imperialism and white supremacy. I have conducted participatory observation fieldwork, interviews, investigated archives, made maps via Emoji Mapping and Social Explorer; I offer visual and historical analysis to demonstrate the social realities that Black and Brown creative communities have forged for the past 237 years in what is now Los Angeles.

basic data for use in code for experimental data analysis for manuscript under revision: Dynamic pathogen detection and social feedback shape collective hygiene in ants Casillas-Pérez B, Boďová K, Grasse AV, Tkačik G, Cremer S

A map used in the Hazard Risk Assessment app and the Hazard Explorer app to visualize social vulnerability.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

Social vulnerability refers to the social, economic, and cultural factors that influence access to resources and influence the ability of individuals, households, or communities to prevent, respond to, and recover from events such as wildfire (Coughlan et al., 2019; Cutter et al., 2003). Some examples of social, economic, or cultural factors that may influence social vulnerability to wildfire include income, language proficiency, cultural and psychological relationships to fire and land management, and level of trust in government (Coughlan et al., 2019). The SVI map layer developed for SB 762 identifies areas in the state that may be more vulnerable to the impacts of wildfire following the methodologies of the Centers for Disease Control (CDC) and Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index (SVI) (Centers for Disease Control Social Vulnerability Index 2018 Documentation, 2022) which was initially developed by Flanagan et al. (2011) for disaster risk management. NOTE: The SVI dataset within the Oregon Explorer tool underwent an update on February 5, 2024 to rectify inaccuracies in the original data. The initial SVI layer computations omitted data pertaining to the indicator "adults over age 65." We strongly recommend individuals who downloaded SVI data prior to this update revise their records accordingly.

An English translation of this metadata is available below after the french version. Les séquences de cultures s'appuient sur les données du Registre Parcellaire Graphique fournies par l'Agence de Service et de Paiement pour les années 2007 à 2014 et par l'IGN pour les années 2015 à 2019. Le traitement des données a été réalisé avec l'outil RPG Explorer développé par l'UMR SADAPT (version 1.10.53). Pour chaque département de l'hexagone on fournit les séquences de cultures sur les 3 périodes 2007-2014 ; 2015-2019 et 2007-2019. Ces données sont établies au niveau de l'entité spatiale élémentaire résultant de l'intersection des parcelles (2015-2019) et îlots (2007-2014) déclarés chaque année. Cette entité est associée à un identifiant unique correspondant à la concaténation des numéros pris par les îlots/parcelles sur chacune des années de la période considérée. Pour chacune des 3 périodes on dispose de deux lots de données complémentaires (1) le shape file départemental des parcelles d'intersection avec le numéro unique de chaque parcelle d'intersection (1) le fichier csv donnant pour chaque numéro unique de parcelle d'intersection les séquences de cultures. Les cultures sont décrites selon 28 groupes cultures .pour la période 2007-2014 et pour plus de 300 cultures pour la période 2015-2019. Pour la période 2007-2019 les cultures sont décrites selon deux modalités : 28 groupes cultures sur l'ensemble de la période + détail sur la période 2015-2019. L'information sur l'ensemble des codes utilisés pour décrire les cultures est donnée dans le fichier List_cultures. Toute surface agricole n'est pas forcément déclarée chaque année. Quand une surface n'est pas déclarée une année son numéro de parcelle/îlot prend la mention "abs" (absent). Il en résulte que les séquences ne peuvent, au mieux, concerner que les surfaces déclarées. Ces séquences sont donc interrompues en cas d'absence de déclaration. Depuis 2015 on n'a qu'une seule culture par polygone (Parcelle) alors que de 2007 à 2014 on pouvait avoir plusieurs groupes cultures déclarés par îlot sans pour autant connaître la délimitation spatiale des différentes parcelles culturales concernées. Les séquences de cultures obtenues sur cette 2007-2014 sont établie d'après un modèle de mise en correspondance des surfaces déclarées d'une année à l'autre sans assurance que l'identité des surfaces correspondent à une identité de parcelles d'une année à l'autre. Le modèle détaillé est explicité dans Levavasseur et al. 2016. La qualité de la séquence est spécifié par la variable qualif_seq qui indique quelle règle (de 1 à 10) a été mobilisée pour établir cette séquence. Si plusieurs règles sont mobilisées pour établir une séquence (ex règle 1 sur 2007-2008 et règle 2 sur 2008-2014) on retient la règle de rang la plus élevée pour qualifier l'ensemble (pour l'ex règle 2 pour 2007-2014). Les règles sont appliquées les unes après les autres de la règle 1 à la règle 10 sachant que les surfaces de séquences obtenues avec une règle sont retirées des déclarations de surface avant d'appliquer la règle suivante. L'algorithme s'arrête quand il a reconnu toutes les surfaces. Si la reconnaissance n'est pas possible on affecte la valeur 8 à la variable qualif_seq. Dès qu'une séquence de 2 années a pu être déterminée qualif_seq prend la valeur de la règle qui a permis d'établir la séquence en question. La surface affectée à une séquence est le minimum des surfaces de chacune des cultures/groupes cultures concernés par la séquence parmi toutes les années de la séquence. Détail des règles mobilisées dans l'ordre suivant : 1,2,3,9,4,5,6,7,10,8 Règle 1: "Une culture par îlot et par an" une seule culture (A) en année n et une seule (B) en année n+1. On affecte la séquence A-B Règle 2: "Surface égale entre les années" Deux cultures par an, réparties sur des surfaces égales d’une année à l’autre (9 ha -> 9 ha, 10 -> 10ha) mais différentes l’une de l’autre (9 ha ≠ de 10 ha), correspondant à deux séquences de type 2 : - 9 ha d’une culture 1 en année n suivie d’une culture 5 en année n+1, - 10 ha de culture 2 en année n suivie d’une culture 2 en année n +1. Règle 3:"Agrégation / désagrégation surface égale" Une culture en année n se « désagrège » en deux cultures en année n+1, de surface totale égale, correspondant à deux séquences de type 3 . Exemple 19 ha de culture 1 en année n puis 9 ha d’une culture 5 associé à 10 ha de culture 2 en année n+1 font 2 séquences de règle 3. Règle 9: "Identification des cultures pérennes" Deux cultures par an, dont des cultures pérennes (identifiées dans RPG Explorer) réparties sur des surfaces différentes (5 ha de culture 18 en année n ≠ 4 ha de culture 18 en année n+1) , correspondant à une séquence de 4 ha de type 9. Règle 4: "Surfaces similaires à X%". Deux cultures par an, réparties sur des surfaces différentes (7 ha de culture 1 ≠ 7,1 ha de culture 5 et 12 ha de culture 2 ≠ 11,8 ha de culture 3) mais similaires à X % d’une année sur l’autre,...

The Explorer Second Issue

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

EN.POP.DNST. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.