Ploting points

(adding awc level)

This visualization presents an interactive choropleth map of Palghar district, India, showing child health metrics such as stunting and underweight prevalence across administrative levels (block, project, beat, and AWC). Users can select a month and health category via dropdowns, and a slider filters the displayed range. The map, built with D3 v5 and SVG, uses a Mercator projection (`d3.geoPath`, `d3.geoMercator`) and updates polygon colors and labels dynamically. A tooltip, data table, and zoom functionality (`d3.zoom`) enhance exploration of the GeoJSON-based geographic and attribute data.

75% match

Map drill down

This example demonstrates a drill-down choropleth map of Palghar district blocks, colored by data values from an external CSV. It shows a tooltip on hover and supports pan/zoom interaction. Built with D3 v5, it uses `d3.geoMercator`, `d3.geoPath`, and `d3.zoom` to render and manipulate SVG paths from GeoJSON data, while `d3.csv` and `d3.json` merge the dataset with geographic boundaries.

72% match

level 1 with all features

This visualization shows a choropleth map of Palghar district, India, displaying nutritional indicators for children across administrative blocks and projects. Users can switch between seven categories, including stunting, wasting, and underweight rates, using a dropdown menu. A slider filters the color scale to focus on specific value ranges, while hovering reveals tooltips with region names and percentages. The map uses D3 v5 with a Mercator projection and `d3.geoPath` to render GeoJSON data. SVG polygons and text labels animate color changes and update values when switching indicators, with zoom support for detailed inspection.

Map making

This interactive SVG map displays administrative blocks in the Palghar district, with each region rendered as a red path using D3’s `geoMercator` projection and `geoPath` generator. The map supports zoom and pan through D3’s `zoom` event, transforming the group element. Block boundaries come from `block.geojson`, while the accompanying `data.csv` provides block names and two numeric columns, though the visualization currently styles all shapes uniformly rather than encoding these values.

SShweta-tech

test geomercator

This example displays a choropleth map of Mumbai's ward boundaries using a Mercator projection, with ward numbers labeled at each polygon's centroid. The map uses `d3.geoMercator` and `d3.geoPath` to render geographic data loaded from a local GeoJSON file, styling wards in grey or green depending on their `prabhag_no` property. Clicking a highlighted ward opens an external link, demonstrating basic interactivity with SVG event handlers. The visualization applies a custom scale and centering to focus on the city region.

async data-read

This visualization shows child malnutrition indicators across administrative blocks in Palghar, India, with monthly data for August, September, and October. It uses D3 v5 to merge CSV data with GeoJSON boundaries for both block-level and project-level regions. The SVG map renders geographic features through d3.geoPath, while asynchronous data loading via d3.json and d3.csv enables the combination of spatial and tabular datasets.

basic map

This choropleth map shows block-level data for the Palghar district, with each polygon colored green, yellow, or red based on the value of the `c1` attribute from a CSV file. Hovering over a region triggers a tooltip displaying the block name and its stunted child percentage. Built with D3 v5, the visualization uses `d3.geoMercator` and `d3.geoPath` to project GeoJSON shapes and renders SVG paths with a zoom transform. Data is joined by matching the `Block` names between the CSV and GeoJSON properties.

70% match