Population Demographics | Saudi Arabia

Categories:

Demographics

Population Demographics data in Dammam, Saudi Arabia – POI data

Population demographics for Saudi Arabia in a high-resolution data format disaggregated to be high resolution of 100 meter

Business Model:

One time purchase

Aggregation Date:

12 May 2023

Raw Data Table

longitude	latitude	female_0	female_1	female_5	female_10	female_15	female_20	female_25	female_30	female_35	female_40	female_45	female_50	female_55	female_60	female_65	female_70	female_75	female_80	male_0	male_1	male_5	male_10	male_15	male_20	male_25	male_30	male_35	male_40	male_45	male_50	male_55	male_60	male_65	male_70	male_75	male_80
49.74833315	26.6775001	0.152362	0.581351	0.672585	0.594218	0.50346	0.567501	0.723531	0.714925	0.659219	0.621064	0.540771	0.382602	0.266809	0.153665	0.105486	0.0537414	0.0308642	0.0241722	0.170959	0.652961	0.795025	0.706033	0.607925	0.722746	1.00597	1.28052	1.26541	1.2312	0.971141	0.640242	0.442138	0.24725	0.152973	0.0645099	0.0412962	0.0338261
49.74833315	26.67666676	0.159109	0.607094	0.702369	0.620531	0.525753	0.592631	0.75557	0.746583	0.68841	0.648566	0.564717	0.399544	0.278624	0.16047	0.110157	0.0561212	0.0322309	0.0252426	0.17853	0.681875	0.830231	0.737297	0.634845	0.75475	1.05052	1.33723	1.32144	1.28572	1.01414	0.668593	0.461717	0.258198	0.159747	0.0673665	0.0431248	0.035324
49.74916648	26.67666676	0.173956	0.663746	0.767911	0.678437	0.574815	0.647933	0.826077	0.816251	0.75265	0.709087	0.617414	0.436828	0.304624	0.175444	0.120436	0.0613582	0.0352386	0.0275981	0.195189	0.745505	0.907705	0.806099	0.694086	0.82518	1.14855	1.46201	1.44475	1.4057	1.10878	0.730984	0.504803	0.282293	0.174654	0.0736528	0.0471491	0.0386203
49.74999982	26.67666676	0.173703	0.662777	0.766791	0.677447	0.573976	0.646988	0.824872	0.81506	0.751552	0.708053	0.616513	0.43619	0.30418	0.175188	0.120261	0.0612687	0.0351871	0.0275579	0.194905	0.744418	0.90638	0.804923	0.693074	0.823977	1.14687	1.45988	1.44265	1.40365	1.10716	0.729917	0.504066	0.281881	0.174399	0.0735454	0.0470803	0.0385639
49.78333315	26.64333343	0.156525	0.597234	0.690962	0.610453	0.517215	0.583006	0.743299	0.734458	0.67723	0.638032	0.555546	0.393055	0.274099	0.157864	0.108368	0.0552098	0.0317074	0.0248326	0.17563	0.670801	0.816747	0.725323	0.624534	0.742492	1.03346	1.31551	1.29998	1.26484	0.997674	0.657735	0.454218	0.254005	0.157153	0.0662724	0.0424245	0.0347503
49.78583315	26.64333343	0.183989	0.702025	0.812198	0.717564	0.607965	0.6853	0.873718	0.863326	0.796057	0.749981	0.653022	0.46202	0.322192	0.185562	0.127382	0.0648968	0.0372708	0.0291898	0.206446	0.7885	0.960053	0.852588	0.734115	0.87277	1.21479	1.54633	1.52807	1.48677	1.17273	0.773141	0.533915	0.298573	0.184727	0.0779005	0.0498683	0.0408476
49.78666648	26.64333343	0.187107	0.713922	0.825962	0.729724	0.618269	0.696914	0.888525	0.877957	0.809548	0.762692	0.664089	0.46985	0.327653	0.188707	0.129541	0.0659967	0.0379025	0.0296845	0.209945	0.801863	0.976324	0.867037	0.746557	0.887561	1.23537	1.57254	1.55397	1.51197	1.1926	0.786244	0.542964	0.303633	0.187857	0.0792207	0.0507134	0.0415398
49.78583315	26.6425001	0.185313	0.707077	0.818043	0.722728	0.612341	0.690232	0.880006	0.869539	0.801786	0.755379	0.657721	0.465345	0.324511	0.186898	0.128299	0.0653639	0.037539	0.0293998	0.207932	0.794174	0.966962	0.858724	0.739398	0.879051	1.22353	1.55746	1.53907	1.49747	1.18117	0.778705	0.537758	0.300721	0.186056	0.0784611	0.0502271	0.0411415
49.78666648	26.6425001	0.186007	0.709724	0.821105	0.725433	0.614633	0.692816	0.8833	0.872794	0.804788	0.758207	0.660183	0.467087	0.325726	0.187598	0.128779	0.0656086	0.0376796	0.0295099	0.20871	0.797147	0.970582	0.861938	0.742166	0.882342	1.22811	1.56329	1.54483	1.50308	1.18559	0.78162	0.539771	0.301847	0.186753	0.0787549	0.0504152	0.0412956
49.78749982	26.6425001	0.195266	0.745056	0.861981	0.761547	0.645231	0.727306	0.927273	0.916243	0.844851	0.795952	0.693049	0.49034	0.341941	0.196936	0.13519	0.0688747	0.0395553	0.030979	0.2191	0.836831	1.0189	0.904847	0.779113	0.926266	1.28925	1.64111	1.62174	1.5779	1.24461	0.820531	0.566642	0.316874	0.19605	0.0826754	0.0529249	0.0433513
49.78833315	26.6425001	0.211738	0.807906	0.934695	0.825788	0.69966	0.788659	1.00549	0.993535	0.91612	0.863096	0.751512	0.531703	0.370786	0.213549	0.146594	0.0746848	0.0428921	0.0335923	0.237583	0.907423	1.10485	0.981177	0.844836	1.0044	1.398	1.77955	1.75854	1.71101	1.3496	0.889748	0.614442	0.343604	0.212588	0.0896497	0.0573895	0.0470083
49.78583315	26.64166676	0.18563	0.708285	0.819441	0.723963	0.613387	0.691412	0.88151	0.871025	0.803156	0.75667	0.658845	0.46614	0.325065	0.187217	0.128518	0.0654756	0.0376032	0.0294501	0.208287	0.795531	0.968615	0.860191	0.740662	0.880553	1.22562	1.56012	1.5417	1.50003	1.18318	0.780035	0.538677	0.301235	0.186374	0.0785952	0.050313	0.0412118
49.78666648	26.64166676	0.187345	0.714832	0.827014	0.730654	0.619056	0.697802	0.889657	0.879075	0.810579	0.763663	0.664935	0.470449	0.32807	0.188948	0.129706	0.0660807	0.0379507	0.0297223	0.210212	0.802884	0.977567	0.868141	0.747508	0.888692	1.23695	1.57454	1.55595	1.51389	1.19412	0.787245	0.543656	0.30402	0.188097	0.0793216	0.050778	0.0415928
49.78749982	26.64166676	0.183046	0.698427	0.808035	0.713886	0.60485	0.681788	0.869241	0.858902	0.791977	0.746138	0.649675	0.459653	0.320541	0.184611	0.126729	0.0645643	0.0370798	0.0290402	0.205388	0.784459	0.955133	0.848219	0.730353	0.868297	1.20856	1.53841	1.52024	1.47915	1.16672	0.769179	0.531179	0.297043	0.18378	0.0775013	0.0496127	0.0406383
49.78833315	26.64166676	0.186224	0.710552	0.822064	0.72628	0.61535	0.693625	0.884331	0.873813	0.805727	0.759092	0.660954	0.467633	0.326106	0.187816	0.128929	0.0656852	0.0377236	0.0295443	0.208954	0.798078	0.971715	0.862944	0.743033	0.883372	1.22954	1.56511	1.54664	1.50483	1.18697	0.782532	0.540401	0.3022	0.186971	0.0788468	0.050474	0.0413438
49.78916648	26.64166676	0.187875	0.716855	0.829355	0.732722	0.620808	0.699777	0.892175	0.881563	0.812873	0.765824	0.666816	0.47178	0.328998	0.189482	0.130073	0.0662678	0.0380581	0.0298064	0.210807	0.805156	0.980334	0.870598	0.749623	0.891207	1.24045	1.579	1.56035	1.51818	1.1975	0.789473	0.545194	0.30488	0.188629	0.0795461	0.0509217	0.0417105
49.78583315	26.64083343	0.185211	0.706689	0.817594	0.722331	0.612005	0.689853	0.879523	0.869061	0.801346	0.754964	0.65736	0.46509	0.324333	0.186795	0.128228	0.065328	0.0375184	0.0293837	0.207818	0.793738	0.966432	0.858252	0.738993	0.878568	1.22286	1.5566	1.53823	1.49665	1.18052	0.778277	0.537463	0.300556	0.185954	0.0784181	0.0501996	0.041119
49.78666648	26.64083343	0.186337	0.710986	0.822565	0.726723	0.615726	0.694048	0.884871	0.874346	0.806218	0.759555	0.661357	0.467918	0.326305	0.187931	0.129008	0.0657252	0.0377466	0.0295624	0.209082	0.798565	0.972308	0.863471	0.743486	0.88391	1.23029	1.56607	1.54758	1.50575	1.1877	0.78301	0.540731	0.302384	0.187085	0.0788949	0.0505048	0.041369
49.78749982	26.64083343	0.186019	0.709773	0.821162	0.725483	0.614676	0.692864	0.883361	0.872854	0.804843	0.758259	0.660229	0.46712	0.325748	0.18761	0.128788	0.0656131	0.0376822	0.0295119	0.208725	0.797202	0.970649	0.861998	0.742218	0.882403	1.22819	1.5634	1.54494	1.50318	1.18567	0.781674	0.539808	0.301868	0.186766	0.0787603	0.0504187	0.0412984
49.78833315	26.64083343	0.186019	0.709773	0.821162	0.725483	0.614676	0.692864	0.883361	0.872854	0.804843	0.758259	0.660229	0.46712	0.325748	0.18761	0.128788	0.0656131	0.0376822	0.0295119	0.208725	0.797202	0.970649	0.861998	0.742218	0.882403	1.22819	1.5634	1.54494	1.50318	1.18567	0.781674	0.539808	0.301868	0.186766	0.0787603	0.0504187	0.0412984
49.78916648	26.64083343	0.185403	0.707421	0.81844	0.723079	0.612638	0.690568	0.880434	0.869962	0.802176	0.755746	0.658041	0.465572	0.324669	0.186989	0.128361	0.0653957	0.0375573	0.0294141	0.208033	0.79456	0.967433	0.859141	0.739758	0.879478	1.22412	1.55822	1.53982	1.4982	1.18174	0.779083	0.538019	0.300868	0.186147	0.0784993	0.0502516	0.0411615
49.78999982	26.64083343	0.187873	0.716845	0.829343	0.732711	0.6208	0.699767	0.892162	0.88155	0.812862	0.765814	0.666807	0.471774	0.328994	0.18948	0.130071	0.0662668	0.0380576	0.029806	0.210804	0.805145	0.98032	0.870586	0.749612	0.891194	1.24043	1.57897	1.56033	1.51816	1.19748	0.789462	0.545186	0.304876	0.188626	0.079545	0.050921	0.0417099
49.78583315	26.6400001	0.18607	0.709966	0.821385	0.72568	0.614842	0.693052	0.883601	0.873091	0.805061	0.758465	0.660408	0.467246	0.325837	0.187661	0.128823	0.0656309	0.0376924	0.0295199	0.208781	0.797419	0.970913	0.862232	0.742419	0.882642	1.22853	1.56382	1.54536	1.50359	1.18599	0.781886	0.539955	0.30195	0.186816	0.0787817	0.0504323	0.0413096
49.78666648	26.6400001	0.182023	0.694524	0.803519	0.709896	0.601469	0.677978	0.864382	0.854101	0.787551	0.741968	0.646044	0.457084	0.31875	0.18358	0.126021	0.0642034	0.0368726	0.0288779	0.20424	0.780074	0.949795	0.843478	0.726271	0.863444	1.20181	1.52981	1.51175	1.47088	1.1602	0.76488	0.528211	0.295382	0.182753	0.0770681	0.0493354	0.0404111

Key Stats about Data

Column name	Description	Type
longitude	Geographical Longitude of the center of the grid	float
latitude	Geographical Latitude of the center of the grid	float
female_0	Female population in 100 meters of age 0 – 1 years old	float
female_1	Female population in 100 meters of age 1-5 years old	float
female_5	Female population in 100 meters of age 5-10 years old	float
female_10	Female population in 100 meters of age 10 – 15 years old	float
female_15	Female population in 100 meters of age 15 – 20 years old	float
female_20	Female population in 100 meters of age 20 – 25 years old	float
female_25	Female population in 100 meters of age 25 – 30 years old	float
female_30	Female population in 100 meters of age 30 – 35 years old	float
female_35	Female population in 100 meters of age 35- 40 years old	float
female_40	Female population in 100 meters of age 40 – 45 years old	float
female_45	Female population in 100 meters of age 45 – 50 years old	float
female_50	Female population in 100 meters of age 50 years old	float
female_55	Female population in 100 meters of age 55 years old	float
female_60	Female population in 100 meters of age 60 years old	float
female_65	Female population in 100 meters of age 65 years old	float
female_70	Female population in 100 meters of age 70 years old	float
female_75	Female population in 100 meters of age 75 years old	float
female_80	Female population in 100 meters of age 80 years old	float
male_0	Male population in 100 meters of age 0 – 1 years old	float
male_1	Male population in 100 meters of age 1-5 years old	float
male_5	Male population in 100 meters of age 5-10 years old	float
male_10	Male population in 100 meters of age 10 – 15 years old	float
male_15	Male population in 100 meters of age 15 – 20 years old	float
male_20	Male population in 100 meters of age 20 – 25 years old	float
male_25	Male population in 100 meters of age 25 – 30 years old	float
male_30	Male population in 100 meters of age 30 – 35 years old	float
male_35	Male population in 100 meters of age 35- 40 years old	float
male_40	Male population in 100 meters of age 40 – 45 years old	float
male_45	Male population in 100 meters of age 45 – 50 years old	float
male_50	Male population in 100 meters of age 50 years old	float
male_55	Male population in 100 meters of age 55 years old	float
male_60	Male population in 100 meters of age 60 years old	float
male_65	Male population in 100 meters of age 65 years old	float
male_70	Male population in 100 meters of age 70 years old	float
male_75	Male population in 100 meters of age 75 years old	float
male_80	Male population in 100 meters of age 80 years old	float

Use Cases

Urban Planning for Elderly-Friendly Infrastructure

Utilize demographic data to identify areas with a high population of individuals aged 65 and above. Plan and design infrastructure enhancements such as accessible pathways, seating, and recreational facilities to cater to the needs of this age group.

Retail and Service Business Planning

Guide business planning by understanding local demographics. Determine the demand for specific products and services based on the age distribution in different areas, enabling businesses to cater to the unique preferences of residents.

Age-Driven Hospitality Services

Create unique hospitality experiences using demographic insights. Develop hotel packages, travel tours, and entertainment offerings that cater to the preferences of distinct age groups, ensuring memorable experiences for every guest.

Retail Location Optimization

Optimize store locations based on age distribution data. Choose prime retail spots in areas with a higher concentration of specific age groups, ensuring maximum foot traffic and tailored product offerings. View this data on interactive dashboard to assess population distribution and evaluate asset placement and new store opening