https://hazelhanzhi.com hazelzhang Fri, 02 Jun 2023 19:29:48 +0000 https://hazelhanzhi.com en https://hazelhanzhi.com/Intro Fri, 26 May 2023 02:33:11 +0000 hazelzhang https://hazelhanzhi.com/Intro Step Into My World where I Spin Data 📊 , Weave Code 🧑‍💻, Conjure Maps 🌎️, Design Wonders 🪄,and transforming multidisciplinary fields into Insights. Scroll to see more︎︎︎ https://hazelhanzhi.com/Home-Image-Scroll Fri, 26 May 2023 02:33:08 +0000 hazelzhang https://hazelhanzhi.com/Home-Image-Scroll <img width="2466" height="1270" width_o="2466" height_o="1270" data-src="https://freight.cargo.site/t/original/i/8119586ded5081882dc43ee2ac604fbd282eb0bfe6d41429092c1f8e0bf35994/Snipaste_2023-05-30_14-25-35.jpg" data-mid="180458527" border="0" data-scale="90" src="https://freight.cargo.site/w/1000/i/8119586ded5081882dc43ee2ac604fbd282eb0bfe6d41429092c1f8e0bf35994/Snipaste_2023-05-30_14-25-35.jpg" />Earthquake Faults and Folds in US <img width="1545" height="980" width_o="1545" height_o="980" data-src="https://freight.cargo.site/t/original/i/1b7811385845cc203309a6055bbeed76aa397d13a71d090f0b6e53dce0acafe9/squ2.jpg" data-mid="180007609" border="0" data-scale="31" alt="Squarriel Colony Analysis in Central Park, NY" data-caption="Squarriel Colony Analysis in Central Park, NY" src="https://freight.cargo.site/w/1000/i/1b7811385845cc203309a6055bbeed76aa397d13a71d090f0b6e53dce0acafe9/squ2.jpg" /><img width="1313" height="1014" width_o="1313" height_o="1014" data-src="https://freight.cargo.site/t/original/i/32c06e9b3c86b19152bc315094512c384ba0907f6c17d9327ac54abb054de029/squ1.jpg" data-mid="180008854" border="0" data-scale="25" alt="Squarriel Colony Analysis in Central Park, NY" data-caption="Squarriel Colony Analysis in Central Park, NY" src="https://freight.cargo.site/w/1000/i/32c06e9b3c86b19152bc315094512c384ba0907f6c17d9327ac54abb054de029/squ1.jpg" /><img width="1368" height="778" width_o="1368" height_o="778" data-src="https://freight.cargo.site/t/original/i/c0103843c5f767800b9223f1bb6a96df0fee016e536cd943ef4b6175c7115994/squ3.jpg" data-mid="180007571" border="0" data-scale="34" src="https://freight.cargo.site/w/1000/i/c0103843c5f767800b9223f1bb6a96df0fee016e536cd943ef4b6175c7115994/squ3.jpg" />Squarriel Colony Analysis in Central Park, NY &nbsp; <img width="1920" height="929" width_o="1920" height_o="929" data-src="https://freight.cargo.site/t/original/i/a4f5a020d10622f1665b80b1999d16571344b0b9ac669f66c3feab48e1b3307d/js.jpg" data-mid="181284096" border="0" data-scale="91" src="https://freight.cargo.site/w/1000/i/a4f5a020d10622f1665b80b1999d16571344b0b9ac669f66c3feab48e1b3307d/js.jpg" /> 311 Data Visualization <img width="2486" height="1242" width_o="2486" height_o="1242" data-src="https://freight.cargo.site/t/original/i/6c61b8efa366c9db67b1b88796fa85632b0bdfb33e2c309de4275a161dcefccc/capstoneGIS.png" data-mid="216108355" border="0" data-scale="92" src="https://freight.cargo.site/w/1000/i/6c61b8efa366c9db67b1b88796fa85632b0bdfb33e2c309de4275a161dcefccc/capstoneGIS.png" />&nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; Traffic Risk Prediction&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; <img width="2556" height="2979" width_o="2556" height_o="2979" data-src="https://freight.cargo.site/t/original/i/f9a327f0dcac33de316730f66442900f2919da7eb75b25411b9574527c4be968/covercapstone.png" data-mid="180501153" border="0" data-scale="87" src="https://freight.cargo.site/w/1000/i/f9a327f0dcac33de316730f66442900f2919da7eb75b25411b9574527c4be968/covercapstone.png" /> Traffic Risk Prediction <img width="1343" height="760" width_o="1343" height_o="760" data-src="https://freight.cargo.site/t/original/i/5d1394227cfecb414f0cd490f15f4a917a4542a690679ed3bef7bce25135b224/Train4.png" data-mid="180501251" border="0" data-scale="83" src="https://freight.cargo.site/w/1000/i/5d1394227cfecb414f0cd490f15f4a917a4542a690679ed3bef7bce25135b224/Train4.png" /> Forcast Metro Train Delays, NY&nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; <img width="1121" height="741" width_o="1121" height_o="741" data-src="https://freight.cargo.site/t/original/i/2f677cb6ef452b07dbb301b547bc3d5b7d14f4dc70fcb6be29b7d7523eb81d3f/SpatialRisk.jpg" data-mid="180009594" border="0" data-scale="70" src="https://freight.cargo.site/w/1000/i/2f677cb6ef452b07dbb301b547bc3d5b7d14f4dc70fcb6be29b7d7523eb81d3f/SpatialRisk.jpg" /> Spatial Risk Prediction&nbsp; &nbsp; &nbsp; &nbsp;&nbsp; <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/f95ada266a00ab8302437292ccebf0fcd723328fcc86b4a03f792ba27bff2fcd/houseprice2.png" data-mid="180822369" border="0" data-scale="79" src="https://freight.cargo.site/w/1000/i/f95ada266a00ab8302437292ccebf0fcd723328fcc86b4a03f792ba27bff2fcd/houseprice2.png" /> Anticipated Housing Market Prices <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/650f73b9006731113663cc4c70295ddaa7629c21e122697885aa8523960de1be/goodness_metrics-1.png" data-mid="180754762" border="0" data-scale="79" src="https://freight.cargo.site/w/1000/i/650f73b9006731113663cc4c70295ddaa7629c21e122697885aa8523960de1be/goodness_metrics-1.png" /> Target House Subsidy <img width="2512" height="1324" width_o="2512" height_o="1324" data-src="https://freight.cargo.site/t/original/i/7e05802a52763364b29d47e6bca7af13b18e6bb593209cdfacfded34f514dfb9/MuniMap.png" data-mid="216109185" border="0" data-scale="88" src="https://freight.cargo.site/w/1000/i/7e05802a52763364b29d47e6bca7af13b18e6bb593209cdfacfded34f514dfb9/MuniMap.png" />Muni Index - Digital Advancement Institute &nbsp; &nbsp; <img width="1902" height="1345" width_o="1902" height_o="1345" data-src="https://freight.cargo.site/t/original/i/4d76734cdb62322d240d7b18d553d5334337f036801e17e66a7169705684e957/school.jpg" data-mid="215229933" border="0" data-scale="61" src="https://freight.cargo.site/w/1000/i/4d76734cdb62322d240d7b18d553d5334337f036801e17e66a7169705684e957/school.jpg" /> https://hazelhanzhi.com/Info Fri, 26 May 2023 02:33:09 +0000 hazelzhang https://hazelhanzhi.com/Info I Am A Rolling One - I came up with this sentence when I was told why the wheels are round in my young age. “I want to be a wheel”, I thought. Today, my interpretation of that early sentiment has evolved to, 'I am a rolling one.' This signifies my eagerness to continuously move forward, grow, and welcome new adventures. Presently, I believe this perfectly encapsulates who I am: studied in data analytics, urban planning, remote sensing, coding, architecture, finance... I believe I have a curiosity with no bounds. I love hiking, running, photography, crocheting, planting, and continuing... So yes, I always would like to&nbsp; adapt and change continuously, to make meaningful contributions to the world.&nbsp; <img width="828" height="817" width_o="828" height_o="817" data-src="https://freight.cargo.site/t/original/i/a9533636435104e1907627d763b540010c0afe67c11d86de7c8e98103d10c2a6/sketch.png" data-mid="180504086" border="0" src="https://freight.cargo.site/w/828/i/a9533636435104e1907627d763b540010c0afe67c11d86de7c8e98103d10c2a6/sketch.png" /> https://hazelhanzhi.com/Traffic-Risk-Prediction Fri, 26 May 2023 02:33:09 +0000 hazelzhang https://hazelhanzhi.com/Traffic-Risk-Prediction 1. Traffic Risk Prediction involved Children Pedestrians in Reading, PA R; JavaScript; Google Cloud Platform&nbsp;MUSA Captone project&nbsp; &nbsp; ArcGIS StoryMap Link R Markdown Link <img width="2556" height="2979" width_o="2556" height_o="2979" data-src="https://freight.cargo.site/t/original/i/d284f3acfafc42fda269d19b2c7c38b33bd836717615ca348e848219b6219d16/covercapstone.png" data-mid="181279723" border="0" data-scale="43" src="https://freight.cargo.site/w/1000/i/d284f3acfafc42fda269d19b2c7c38b33bd836717615ca348e848219b6219d16/covercapstone.png" /> In the United States, children remains 20% of total 38,680 car crashes records in 2020, which facing a high risk of being involved in traffic accidents. This research project aims to address this issue by focusing on this vulnerable group and developing a precise model to identify potential risks and implement protective measures. <img width="650" height="697" width_o="650" height_o="697" data-src="https://freight.cargo.site/t/original/i/37e9c368fcbf04bf3db06c63abd302c6a23236d29ca163b6c0077b2c3b842256/Snipaste_2023-04-20_21-07-38.jpg" data-mid="180009424" border="0" data-scale="78" src="https://freight.cargo.site/w/650/i/37e9c368fcbf04bf3db06c63abd302c6a23236d29ca163b6c0077b2c3b842256/Snipaste_2023-04-20_21-07-38.jpg" /> <img width="1200" height="1000" width_o="1200" height_o="1000" data-src="https://freight.cargo.site/t/original/i/779fc16d138177d7c1c99f26f339bff8658939e0e2664a68cbd13eefbc2e27b0/Snipaste_2023-04-20_21-08-14.jpg" data-mid="180009425" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/779fc16d138177d7c1c99f26f339bff8658939e0e2664a68cbd13eefbc2e27b0/Snipaste_2023-04-20_21-08-14.jpg" /> <img width="582" height="454" width_o="582" height_o="454" data-src="https://freight.cargo.site/t/original/i/399b3e1087b90054891d1ca431d8668833361577ec765ab4e412986c2e09f032/Snipaste_2023-04-20_21-09-06.jpg" data-mid="180752355" border="0" data-scale="100" src="https://freight.cargo.site/w/582/i/399b3e1087b90054891d1ca431d8668833361577ec765ab4e412986c2e09f032/Snipaste_2023-04-20_21-09-06.jpg" /> Crash Data Aggre gated on Census Tract Level and Road Segment Level Variables Correlation Matrix&nbsp; &nbsp; <img width="1200" height="778" width_o="1200" height_o="778" data-src="https://freight.cargo.site/t/original/i/62f7e10506b74f47f7d77310ddbd24d4f943cd0e2c91c4a411293ccb05c0a1bb/Snipaste_2023-04-20_21-10-02.jpg" data-mid="180752304" border="0" data-scale="78" src="https://freight.cargo.site/w/1000/i/62f7e10506b74f47f7d77310ddbd24d4f943cd0e2c91c4a411293ccb05c0a1bb/Snipaste_2023-04-20_21-10-02.jpg" /> Data Explorer by Census Tract ID&nbsp; &nbsp; Methods Deployed a multilevel model account for hierarchical structure of individual crashes history and neighborhoods by modeling the variation in car crashes at each level, estimating the effects of predictors, and predicting the potential possibility on each road segment. <img width="1207" height="1257" width_o="1207" height_o="1257" data-src="https://freight.cargo.site/t/original/i/6c7d5b95cdc26516db37403dc942b4485a0216d86647d57016aa1730617beb7e/Snipaste_2023-04-20_23-06-15.jpg" data-mid="180751851" border="0" data-scale="72" alt="Methodology" data-caption="Methodology" src="https://freight.cargo.site/w/1000/i/6c7d5b95cdc26516db37403dc942b4485a0216d86647d57016aa1730617beb7e/Snipaste_2023-04-20_23-06-15.jpg" /> Data Pipeline Leveraged Google Cloud Platform to ingest and process streaming data from PennDOT( Pennsylvania Department of Transportation ). By automating the data collection and analysis process, a data pipeline can provide real-time or near real-time insights into traffic patterns and risk factors. This can help transportation agencies and emergency services respond more quickly to accidents or other incidents. <img width="1178" height="667" width_o="1178" height_o="667" data-src="https://freight.cargo.site/t/original/i/45800a2ba47bce2ea17f87178d6f2e09d30e6b93d57e352c723df1c0b5edc7ac/pipeline.jpg" data-mid="180009463" border="0" data-scale="89" src="https://freight.cargo.site/w/1000/i/45800a2ba47bce2ea17f87178d6f2e09d30e6b93d57e352c723df1c0b5edc7ac/pipeline.jpg" /> Results Community economic status is strongly associated with child car accident rates. While the p-value of community-level variables in the linear model may not always meet traditional thresholds of statistical significance, the variance in the multilevel model suggests that a significant portion of the variation in car crash rates can be attributed to communitylevel factors.This research uses MAPE (Mean Absolute Percentage Error) to evaluate the prediction accuracy of multilevel poisson model, which is 0.52. While this indicates that this model is not perfectly accurate, it is still able to effectively identify high-risk areas for car crashes. By comparing the mapping of real car crash history and prediction, we found that the pattern of risk areas identified by multilevel poisson model remained consistent despite the slightly lower predictionaccuracy. This suggests that this model is still able to effectively capture the underlying factors that contribute to car crashes and identify areas where preventative measures should be focused. <img width="1200" height="733" width_o="1200" height_o="733" data-src="https://freight.cargo.site/t/original/i/ba4960bda258e99b9d4108079d47cb813c791bac6c3c226ed93f84133cfbaab4/Snipaste_2023-04-20_21-11-00.jpg" data-mid="180752991" border="0" data-scale="77" src="https://freight.cargo.site/w/1000/i/ba4960bda258e99b9d4108079d47cb813c791bac6c3c226ed93f84133cfbaab4/Snipaste_2023-04-20_21-11-00.jpg" /> Implication Constructed a model for smallsample, low-probability random events. Informed the development of specific interventions such as increasing the number of stop signs, widening walkways, and designing warning facilities tailored for child pedestrians. <img width="1158" height="784" width_o="1158" height_o="784" data-src="https://freight.cargo.site/t/original/i/39038851419af3ff966ba92a0dd899340decb4d37e24676443070a45e448ca37/web.jpg" data-mid="180752567" border="0" src="https://freight.cargo.site/w/1000/i/39038851419af3ff966ba92a0dd899340decb4d37e24676443070a45e448ca37/web.jpg" /> https://hazelhanzhi.com/Forecast-Metro-Train-Delays Fri, 26 May 2023 02:33:09 +0000 hazelzhang https://hazelhanzhi.com/Forecast-Metro-Train-Delays 2. Forecast Metro Train Delays in NJ <img width="1400" height="865" width_o="1400" height_o="865" data-src="https://freight.cargo.site/t/original/i/63b3638c7834eb6d783f8816d8d964672809dd5b8c2eeee98fb26b51579fc25b/Train2.png" data-mid="180007735" border="0" data-scale="73" src="https://freight.cargo.site/w/1000/i/63b3638c7834eb6d783f8816d8d964672809dd5b8c2eeee98fb26b51579fc25b/Train2.png" /> NJ Transit system is a non-cyclical rail network that owns 11 lines and services 162 train stations in New Jersey and connects travelers to New York Penn Station. The region of NJ Transit operations has complex system dynamics that affect thousands of people everyday. Train delays will affect many people’s travel and schedules. Therefore, a good prediction could allow enough time for passengers to consider train delays and anticipate in advance. Delay is the extra time it takes a train to operate on a route due to many factors, such as weather, stations, lines, and equipment. The delay will not only affect the operation of the train but also spread in the section, causing other trains to be late. Train delays will also cause a long time of passenger retention and bring inconvenience. We want to study trends and offer a better understanding of the principal factors that contribute to training delays. Therefore, our goal is to provide a reliable prediction of station delay that can help dispatchers to estimate the train operation status and make reasonable dispatching decisions to improve the operation and service quality of rail transit. UI Design According to this use case, we will focus on Rail Passengers. We design three main functions for them, which is to get real-time train information, offer train delay prediction. And users can click the customize button to get the train report by adding the train they interested. <img width="2418" height="1506" width_o="2418" height_o="1506" data-src="https://freight.cargo.site/t/original/i/bd94b58f89b868290f89a4de3895887a0b105dca8cf6ad0c2380e84eb0c5915a/Train6.png" data-mid="180753314" border="0" data-scale="85" src="https://freight.cargo.site/w/1000/i/bd94b58f89b868290f89a4de3895887a0b105dca8cf6ad0c2380e84eb0c5915a/Train6.png" /> <img width="1656" height="1342" width_o="1656" height_o="1342" data-src="https://freight.cargo.site/t/original/i/f3f878c9ce347d6d90a605f8d90d4cac6c86141f81dde4b285e773c12c999819/Train7.png" data-mid="180753317" border="0" data-scale="83" src="https://freight.cargo.site/w/1000/i/f3f878c9ce347d6d90a605f8d90d4cac6c86141f81dde4b285e773c12c999819/Train7.png" /> <img width="1564" height="1244" width_o="1564" height_o="1244" data-src="https://freight.cargo.site/t/original/i/33ae5c961d96ddca1816c1846b0a2dd2cb5e863b5c05de43ef2646d978e02306/Train8.png" data-mid="180753318" border="0" data-scale="86" src="https://freight.cargo.site/w/1000/i/33ae5c961d96ddca1816c1846b0a2dd2cb5e863b5c05de43ef2646d978e02306/Train8.png" /> Data&nbsp; Exploratory <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/d2ff2d9acf39d362edc623b176ace0d5f01b618cad703c65af4fdd059141065f/Train9.png" data-mid="180753402" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/d2ff2d9acf39d362edc623b176ace0d5f01b618cad703c65af4fdd059141065f/Train9.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/a286cc82563f82f3e53a0e521551575e123253f5a8d39a96637277f388faab94/Train10.png" data-mid="180753411" border="0" src="https://freight.cargo.site/w/1000/i/a286cc82563f82f3e53a0e521551575e123253f5a8d39a96637277f388faab94/Train10.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/ddb59ec6aa667b59d4a549d3e95317878449bf3bfbaef8847988477a308727ba/Train11.png" data-mid="180753493" border="0" src="https://freight.cargo.site/w/1000/i/ddb59ec6aa667b59d4a549d3e95317878449bf3bfbaef8847988477a308727ba/Train11.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/e486266db51f48557146686072166ff6f99a39753f7981e30371f3ec8d9b019b/Train12.png" data-mid="180753494" border="0" src="https://freight.cargo.site/w/1000/i/e486266db51f48557146686072166ff6f99a39753f7981e30371f3ec8d9b019b/Train12.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/0b990d8542276b8753c02bf30f3176c75581ce295d03f267a58e4bbffe71d22f/Train13.png" data-mid="180753498" border="0" src="https://freight.cargo.site/w/1000/i/0b990d8542276b8753c02bf30f3176c75581ce295d03f267a58e4bbffe71d22f/Train13.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/f660c69bf7abf54102c18d3c3f761c079e7ac19acc08eaa249d4979120be81f2/Train14.png" data-mid="180753499" border="0" src="https://freight.cargo.site/w/1000/i/f660c69bf7abf54102c18d3c3f761c079e7ac19acc08eaa249d4979120be81f2/Train14.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/8a98474739c3755ca8ffdf454fc5a85d409f34748e12249a80454cab6781fabd/Train15.png" data-mid="180753503" border="0" src="https://freight.cargo.site/w/1000/i/8a98474739c3755ca8ffdf454fc5a85d409f34748e12249a80454cab6781fabd/Train15.png" /> Model I created 7 regression models to identify the effects of spatial factors, temporal factors and also external features. Model A focuses on just time effects, including temporal controls: hour fixed effects, day of the week.Model B focuses on just space effects with the stations fixed effects, and also includes day of the week and the weather.Model C includes both time and space fixed effects, and also contains weather.Model D focuses on station lags, with both time and space fixed effects, weather and transportation features.Model E focuses on time lags, with both time and space fixed effects, weather, and transportation features.Model F focuses on both time lags and station lags, also includes time and space fixed effects, then contains weather and transportation features.Model G includes both time and space fixed effects, and also both time lags and station lags, then contains weather, transportation features and census factors. Mathematically, we use some common evaluation metrics to examine the performance of models. Mean Absolute Error (MAE): the mean absolute error between observed and predicted values.R-squared (R2): the higher the R-squared, the better the model.Residual Standard Error (RSE): the lower the RSE, the better the model.AIC: the lower the AIC, the better the model. From the summary table, the Model E, F, G have lower AIC value, and R-squared are much higher, compared with Model A, B, C, D. We conclude that adding station lags significantly improves the performance of model. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/058cd193da79ff8f524fe4b0e6c793b50b6c8fc83e0e89bfdcf251c2f95db5f9/Train16.png" data-mid="180753581" border="0" src="https://freight.cargo.site/w/1000/i/058cd193da79ff8f524fe4b0e6c793b50b6c8fc83e0e89bfdcf251c2f95db5f9/Train16.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/017d84dccbb4e8922ec0ab552d8bba3c0fb416766771044d76e88ffa9c0301af/Train17.png" data-mid="180753582" border="0" src="https://freight.cargo.site/w/1000/i/017d84dccbb4e8922ec0ab552d8bba3c0fb416766771044d76e88ffa9c0301af/Train17.png" /> <img width="2099" height="1499" width_o="2099" height_o="1499" data-src="https://freight.cargo.site/t/original/i/dac4b65083d7fb35d0070e0b74ef3824f3f7e5e3cd84842c352f7e598c6eaa2b/train_cover.gif" data-mid="180009036" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/dac4b65083d7fb35d0070e0b74ef3824f3f7e5e3cd84842c352f7e598c6eaa2b/train_cover.gif" /> Main Abs Error in Test Set <img width="2820" height="1576" width_o="2820" height_o="1576" data-src="https://freight.cargo.site/t/original/i/04aa677832f26e44f1e07133ca26cb5e59c8e673d7c04542eb9771a162544364/Train5.png" data-mid="180753116" border="0" data-scale="85" src="https://freight.cargo.site/w/1000/i/04aa677832f26e44f1e07133ca26cb5e59c8e673d7c04542eb9771a162544364/Train5.png" /> Presentation&nbsp; Link https://hazelhanzhi.com/Spatial-Risk-Prediction Fri, 02 Jun 2023 02:33:12 +0000 hazelzhang https://hazelhanzhi.com/Spatial-Risk-Prediction 3. Assult Risk Prediction in Chicago, IL <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/ee5005945c750c02a03a2744682b496452dde2f5a737142452a8bdac2388c817/assult1.png" data-mid="180754016" border="0" data-scale="61" src="https://freight.cargo.site/w/1000/i/ee5005945c750c02a03a2744682b496452dde2f5a737142452a8bdac2388c817/assult1.png" /> A new survey shows that nearly half of Chicago residents feel “very unsafe” in the city as a whole, and less than a quarter of Chicagoans feel safe in the city where they live. In addition, less than 30 percent of residents feel safe in their neighborhoods, while the same survey 2021 fall showed that 45 percent of the public felt safe in their neighborhoods. A geospatial risk model is a regression model. The dependent variable is the occurrence of discrete events like crime, fires, etc. Predictions from these models are interpreted as ‘the forecasted risk/opportunity of that event occurring here’. So I want to find out the regular of assault number that happens on the road. If we use a geo-spatial risk model, would it be possible for the police station to place more policers in places where road assault is likely to occur. Can such a model reduce the occurrence of road assault? In order to avoid other assault information, like assault happens in the apartment or buildings. The dataset only contains assault happens on the street, sidewalk, and alleys. Data Visualization <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/0e9a56fe40d9b843709a70beba09f682486e3365409611bc2ca72a3f19e75572/assult2.png" data-mid="180754165" border="0" src="https://freight.cargo.site/w/1000/i/0e9a56fe40d9b843709a70beba09f682486e3365409611bc2ca72a3f19e75572/assult2.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/8d1ae4ec590664ebcd98bd7ed74e026198c1d6a12135cd47af6cd6ed6ae3a8f8/assult3.png" data-mid="180754192" border="0" src="https://freight.cargo.site/w/1000/i/8d1ae4ec590664ebcd98bd7ed74e026198c1d6a12135cd47af6cd6ed6ae3a8f8/assult3.png" /> Model Evaluation <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/eaed9926c3523b22c8ee132e5bfd928423a22555976db08300e11217dbd025f4/assult5.png" data-mid="180754475" border="0" data-scale="72" src="https://freight.cargo.site/w/1000/i/eaed9926c3523b22c8ee132e5bfd928423a22555976db08300e11217dbd025f4/assult5.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/1ec7902fa75f759e3788ca937a14d41e020176d76c0efadb233f4b50683a91e5/assult4.png" data-mid="180754429" border="0" data-scale="72" src="https://freight.cargo.site/w/1000/i/1ec7902fa75f759e3788ca937a14d41e020176d76c0efadb233f4b50683a91e5/assult4.png" /> <img width="1121" height="741" width_o="1121" height_o="741" data-src="https://freight.cargo.site/t/original/i/e422934248ecbb17455e633b437d985f0f6d4dab14fb054c28bd24290c974556/SpatialRisk.jpg" data-mid="180754417" border="0" data-scale="77" src="https://freight.cargo.site/w/1000/i/e422934248ecbb17455e633b437d985f0f6d4dab14fb054c28bd24290c974556/SpatialRisk.jpg" /> https://hazelhanzhi.com/Anticipated-Housing-Market-Prices Fri, 02 Jun 2023 19:16:01 +0000 hazelzhang https://hazelhanzhi.com/Anticipated-Housing-Market-Prices 4. House Price Prediction in Mecklenburg County, NC <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/70672748662aa62dc57eb5433c0cb40d70ea0614405d9792b8dad79c6f80ab66/houseprice7.png" data-mid="181277011" border="0" data-scale="77" src="https://freight.cargo.site/w/1000/i/70672748662aa62dc57eb5433c0cb40d70ea0614405d9792b8dad79c6f80ab66/houseprice7.png" /> In Mecklenburg County, the housing market shows good trend recent years. Housing Price Prediction is necessary and helpful. The hedonic model is a theoretical framework for predicting home prices by deconstructing house price into the value of its constituent parts. We first make OLS predictions according to the original dataset, and then filter the predictions according to the performance of the r square and MAPE to continuously optimize our model. Data Exploratory In the model, the dependent variable is house sale price. The factors are contains 3 types: 1.Interal characteristics2.Amenities of decision factor3.Spatial structure In the histograms of numeric variables, it can be concluded the distribution of the number of the variables. These sample data are relatively concentrated. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/9a02b7eaf8e3c69b3ec610306956c80f3c1cc867a05322b374f1922b4e6394d5/houseprice3.png" data-mid="181277069" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/9a02b7eaf8e3c69b3ec610306956c80f3c1cc867a05322b374f1922b4e6394d5/houseprice3.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/bba3f0ff3c173dc8bd8d446d3430dfd6be58b03706a046dbe92b0656257daea7/houseprice.png" data-mid="181277211" border="0" src="https://freight.cargo.site/w/1000/i/bba3f0ff3c173dc8bd8d446d3430dfd6be58b03706a046dbe92b0656257daea7/houseprice.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/a822bd32bc5062955b41b604dc585da72d515ce19556791f892bf98c872ed6dc/houseprice4.png" data-mid="181277226" border="0" src="https://freight.cargo.site/w/1000/i/a822bd32bc5062955b41b604dc585da72d515ce19556791f892bf98c872ed6dc/houseprice4.png" /> Correlation Matrix A correlation matrix gives us the pairwise correlation of each set of features in our data. We add the each predictors’R square of correlation matrix in the plot. Our analysis of pairwise correlations between these predictors helps us assess the degree of association between these predictors. At this time, we are going to delete collinear independent(R srquare&gt;0.75 or &lt;-0.75). We choose the length of shape, distance to nearest 3 transit stops, percent of graduate, and move the shape area, percent bachelor degree and distance to nearest others transit stops out at the same time. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/a1cbd0b1276669be63b80789b9ba62d05be895bb5bd746e6c5ccccbf2709adb2/houseprice2.png" data-mid="181277279" border="0" src="https://freight.cargo.site/w/1000/i/a1cbd0b1276669be63b80789b9ba62d05be895bb5bd746e6c5ccccbf2709adb2/houseprice2.png" /> Analyzing Associations We chose four factors that we thought were most relevant to house price, but it appears that school has little effect on home prices. But I wonder what will change in the multi-factor model afterwards. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/1ff3973fe91cc7b6ab6d2148ebc20abb64246e4e699cbf78c16225729944cf70/houseprice5.png" data-mid="181277447" border="0" src="https://freight.cargo.site/w/1000/i/1ff3973fe91cc7b6ab6d2148ebc20abb64246e4e699cbf78c16225729944cf70/houseprice5.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/788268823ef5c443a8ef0e0b044c79647516ac330c80f03960831333d16ca6a3/houseprice6.png" data-mid="181277450" border="0" src="https://freight.cargo.site/w/1000/i/788268823ef5c443a8ef0e0b044c79647516ac330c80f03960831333d16ca6a3/houseprice6.png" /> Model Comparison (OLS &amp; Spatial Lag Models) The distribution of MAE in OLS model is not aggregated enough, which there are still many scattered distributions. We consider that the reasons for this distribution may be: 1. The presence of some extreme values 2. The presence of spatial correlation. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/e40c8dd3176124c3041280f799b3837ea110b141169ed6612fa88a324987b9b3/houseprice8.png" data-mid="181277483" border="0" data-scale="70" src="https://freight.cargo.site/w/1000/i/e40c8dd3176124c3041280f799b3837ea110b141169ed6612fa88a324987b9b3/houseprice8.png" /> After comparing the MAE on the map, and the graphic of price as a function of the spatial lag, the result illustrates prices clustering at different spatial scales. I conclude it is necessary to add the spatial factor into the model. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/2ae827dadfa0f3f7bfab8a94517c702fc86bd3597e71db0c8f9caf818cb6de12/houseprice9.png" data-mid="181277511" border="0" src="https://freight.cargo.site/w/1000/i/2ae827dadfa0f3f7bfab8a94517c702fc86bd3597e71db0c8f9caf818cb6de12/houseprice9.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/8d2acbac462975ae8cd6f4143902bcc73121f4cdc691c9bff74a5ac14f37d81f/houseprice10.png" data-mid="181277514" border="0" src="https://freight.cargo.site/w/1000/i/8d2acbac462975ae8cd6f4143902bcc73121f4cdc691c9bff74a5ac14f37d81f/houseprice10.png" /> Moran’s I Test The Moran’s I test results confirm our interpretation of the map. The Clustered point process yields a middling I of 0.31(Moran’s I value). But a p-value of 0.001 suggests that the observed point process is more clustered than all 999 random permutations (1 / 999 = 0.001) and is statistically significant. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/a31beb94ed98e7cd7fd01fa9ce68ba8c32147ebcc2122bca2c5cf00c4c561075/houseprice11.png" data-mid="181277847" border="0" data-scale="65" src="https://freight.cargo.site/w/1000/i/a31beb94ed98e7cd7fd01fa9ce68ba8c32147ebcc2122bca2c5cf00c4c561075/houseprice11.png" /> Generalizability The AbsError and APE all decrease, which means the Neighborhood Effects model is more accurate on both a dollars and percentage basis. Predicted prices are plotted as a function of observed prices. Recall the purple line represents a would-be perfect fit, while the yellow line represents the predicted fit. <img width="865" height="103" width_o="865" height_o="103" data-src="https://freight.cargo.site/t/original/i/a930ce58f1aa98007d311ac4ff6b886c22b0b662afaeb0a3c25c51638acf6c2c/houseprice12.jpg" data-mid="181277994" border="0" data-scale="100" src="https://freight.cargo.site/w/865/i/a930ce58f1aa98007d311ac4ff6b886c22b0b662afaeb0a3c25c51638acf6c2c/houseprice12.jpg" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/284933b63992aea4215552f249e22092cd0266a8db75ec745d7c358233270f10/houseprice12.png" data-mid="181277976" border="0" data-scale="64" src="https://freight.cargo.site/w/1000/i/284933b63992aea4215552f249e22092cd0266a8db75ec745d7c358233270f10/houseprice12.png" /> We would recommend this model. first we calculated our APE in the test set to be around 0.16, which is an acceptable margin of error. When we compare the baseline model with the neighborhood model, we find that the regression curve becomes very well fitted after adding the spatial factor. the value of r-squared also remains stable at around 0.83. And in terms of generalizability, our model has relatively strong generalizability across different kinds of partitions. However, it is worth mentioning that our model seems to fit better for high-income communities. The reason may be due to the larger amount of data in high-income communities or the richer facilities in high-income communities. There are still some limitations in our model. If we need to create a super model that is broadly applicable to many types of communities, firstly, we are going to find the best predictive features or variables. Secondly, we have to try to inject enough predictive power into the model to make good predictions without over-fitting. https://hazelhanzhi.com/Target-House-Subsidy Fri, 02 Jun 2023 19:17:10 +0000 hazelzhang https://hazelhanzhi.com/Target-House-Subsidy 5. Targeting A House Subsidy To decrease the severe situation in housing and economic problem, HCD wants to improve householders’ conditions by offering a home repair tax credit. Data analytics is helpful in this process. As we all know, the low conversion rate of promotion, offer, and allocation credits will lead to low effectiveness of HCD. The department will pay more because they can not reach out the eligible homeowners. I think data science can be helpful in this condition, especially the logic model, which can categorize people’s willingness to take credit. Data Exploratory <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/9f1f74960e47c5785772f7bd5a7bd6b6a90b232a99d49da2615a61101f5509c0/HouseSubsidy.png" data-mid="181278702" border="0" src="https://freight.cargo.site/w/1000/i/9f1f74960e47c5785772f7bd5a7bd6b6a90b232a99d49da2615a61101f5509c0/HouseSubsidy.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/cf628af1a5c2e849864eec9d8e9095d2720b5aabc4b4c004c7bc32e20157c4de/HouseSubsidy3.png" data-mid="181278732" border="0" src="https://freight.cargo.site/w/1000/i/cf628af1a5c2e849864eec9d8e9095d2720b5aabc4b4c004c7bc32e20157c4de/HouseSubsidy3.png" /> Logistic Model Evaluation Compared two models, previous one’s (before feature engineering) distributions are more concentrated on the average values. So the model before feature engineering performs better in the Cross Validation. The model performs well in people who don’t get the credit. But the people who will get the credit curve is flatten and has a small peak in the low threshold. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/93420099c987590cc331b53bcb9d0c87d2b9f1005a31767c2947ec4d23308528/HouseSubsidy4.png" data-mid="181278809" border="0" data-scale="50" src="https://freight.cargo.site/w/1000/i/93420099c987590cc331b53bcb9d0c87d2b9f1005a31767c2947ec4d23308528/HouseSubsidy4.png" /><img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/324f53fdeb2b0ad9774cfe1df44b1961bff42a946e90a6ab943a4a5984b1a3a2/HouseSubsidy5.png" data-mid="181278812" border="0" data-scale="49" src="https://freight.cargo.site/w/1000/i/324f53fdeb2b0ad9774cfe1df44b1961bff42a946e90a6ab943a4a5984b1a3a2/HouseSubsidy5.png" /> ROC Curve The area under the curve is 0.7072, which is a medium value of AOC. The y-axis of the ROC curve shows the rate of true positives for each threshold from 0.01 to 1. The x-axis shows the rate of false positives for each threshold. This ROC curve below shows the model has a better appearance in a low threshold than high threshold. When the true positive increase in the map, the false positive errors will increase, too. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/4ed292a4e0c32305a25afb517662ee733c9ff6715adef49c5e4252891ab6a039/HouseSubsidy2.png" data-mid="181278893" border="0" data-scale="64" src="https://freight.cargo.site/w/1000/i/4ed292a4e0c32305a25afb517662ee733c9ff6715adef49c5e4252891ab6a039/HouseSubsidy2.png" /> Cost Benefit Analysis This has all been building to an examination of the model in the context of our ad campaign. Let’s set this up to estimate the revenues associated with using this model under the following scenario: -An impression (serving an ad) costs $0.10 -A click brings an estimated $0.35 of revenue per visitor on average. a. Cost/Benefit Equation for Confusion Metric True Positive - Predicted correctly homeowner would enter credit program; allocated the marketing resources, and 25% ultimately achieved the credit. True Negative - Predicted correctly homeowner would not take the credit, no marketing resources were allocated, and no credit was allocated. False Positive - Predicted incorrectly homeowner would take the credit; allocated marketing resources; no credit allocated. False Negative - We predicted that a homeowner would not take the credit but they did. These are likely homeowners who signed up for reasons unrelated to the marketing campaign. Thus, we ‘0 out’ this category, assuming the cost/benefit of this is $0. <img width="871" height="206" width_o="871" height_o="206" data-src="https://freight.cargo.site/t/original/i/f16390ec73b0aff9be3f1be626108021e92867471d4a0a8102cd690da6c6b4d2/HouseSubsidy6.png" data-mid="181278921" border="0" src="https://freight.cargo.site/w/871/i/f16390ec73b0aff9be3f1be626108021e92867471d4a0a8102cd690da6c6b4d2/HouseSubsidy6.png" /> b. Plot Confusion Metric Outcomes of Each Threshold Threshold as a function of Total_Revenue elaborates that total avenue all converge on 0 when the threshold gets bigger. According to my calculate function of revenue, True_Negative and False_Negative make 0 effects on revenue, so they are stable whether how the threshold change. As for True_Positive, when the threshold gets bigger to 1, the count of True_Positive will decrease close to 0, so the revenue will close to 0. The same as False_Positive.&nbsp; This plot can be concluded the total count of negative samples is much bigger than positive samples, which may affect the accuracy difference between negative and positive samples. And both negative and positive results will change a lot in the range of 0.05-0.2 threshold. <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/c6cd954e08f4fc0668b3737aeab5e3719aba9b82e5e2de4a9804f4982ada9df6/HouseSubsidy7.png" data-mid="181278960" border="0" src="https://freight.cargo.site/w/1000/i/c6cd954e08f4fc0668b3737aeab5e3719aba9b82e5e2de4a9804f4982ada9df6/HouseSubsidy7.png" /> <img width="1344" height="960" width_o="1344" height_o="960" data-src="https://freight.cargo.site/t/original/i/d6febc14335eeeae2aef7ee1a0a076dc07e305c962c7b0fd2623dec5f53e2e0b/HouseSubsidy8.png" data-mid="181278963" border="0" src="https://freight.cargo.site/w/1000/i/d6febc14335eeeae2aef7ee1a0a076dc07e305c962c7b0fd2623dec5f53e2e0b/HouseSubsidy8.png" /> https://hazelhanzhi.com/Portfolio Fri, 26 May 2023 02:33:10 +0000 hazelzhang https://hazelhanzhi.com/Portfolio portfolio of architecture and design &nbsp; <img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/c2c7efc9fc241a71c8f155ba81129f1941c2f242d1bfe985c14e1a29b79f0621/Final__01.jpg" data-mid="180755340" border="0" src="https://freight.cargo.site/w/1000/i/c2c7efc9fc241a71c8f155ba81129f1941c2f242d1bfe985c14e1a29b79f0621/Final__01.jpg" /><img width="3308" height="2339" width_o="3308" height_o="2339" data-src="https://freight.cargo.site/t/original/i/338403fec4bf51f90f4a87910a1c408381463b6f9449991067304444da2478b3/Final__02.jpg" data-mid="180755343" border="0" src="https://freight.cargo.site/w/1000/i/338403fec4bf51f90f4a87910a1c408381463b6f9449991067304444da2478b3/Final__02.jpg" /><img width="2530" height="1790" width_o="2530" height_o="1790" data-src="https://freight.cargo.site/t/original/i/5c30f37ba8bfc53ebb0d065d891c11450e052da1e0b6c3f063c7ee50fac88bc6/Final__03.jpg" data-mid="180755347" border="0" src="https://freight.cargo.site/w/1000/i/5c30f37ba8bfc53ebb0d065d891c11450e052da1e0b6c3f063c7ee50fac88bc6/Final__03.jpg" /> <img width="2497" height="1766" width_o="2497" height_o="1766" data-src="https://freight.cargo.site/t/original/i/6afc72359021c6f118b99ea86eb4a720f5484a6864e8679f70681cee370f53eb/Final__04.jpg" data-mid="180755349" border="0" src="https://freight.cargo.site/w/1000/i/6afc72359021c6f118b99ea86eb4a720f5484a6864e8679f70681cee370f53eb/Final__04.jpg" /> <img width="2481" height="1778" width_o="2481" height_o="1778" data-src="https://freight.cargo.site/t/original/i/9bb9252c5cb6ccb0877bad87075f61ab10a610166164f74cbb935c0812294e84/Final__05.jpg" data-mid="180755351" border="0" src="https://freight.cargo.site/w/1000/i/9bb9252c5cb6ccb0877bad87075f61ab10a610166164f74cbb935c0812294e84/Final__05.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/b1ef2608a01ec63541e0b900adc18ff3885281dfe16685f93ccd1da0b2e31de1/Final__09.jpg" data-mid="180755352" border="0" src="https://freight.cargo.site/w/1000/i/b1ef2608a01ec63541e0b900adc18ff3885281dfe16685f93ccd1da0b2e31de1/Final__09.jpg" /><img width="2497" height="1754" width_o="2497" height_o="1754" data-src="https://freight.cargo.site/t/original/i/4bcceb8d82b67250b5c562e76724da4d133e1f4fc76a80de9b7849ce4e46b3b9/Final__10.jpg" data-mid="180755354" border="0" src="https://freight.cargo.site/w/1000/i/4bcceb8d82b67250b5c562e76724da4d133e1f4fc76a80de9b7849ce4e46b3b9/Final__10.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/b63cf34ba11d1847a96d3ad238e34404f9dabcc67dd4d4de8e773b81a3fa6e72/Final__11.jpg" data-mid="180755356" border="0" src="https://freight.cargo.site/w/1000/i/b63cf34ba11d1847a96d3ad238e34404f9dabcc67dd4d4de8e773b81a3fa6e72/Final__11.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/c4d0c0bde729927e10a20cd8c89592b9528cc8bffa5e5dc97fd66846275495f7/Final__12.jpg" data-mid="180755357" border="0" src="https://freight.cargo.site/w/1000/i/c4d0c0bde729927e10a20cd8c89592b9528cc8bffa5e5dc97fd66846275495f7/Final__12.jpg" /><img width="2481" height="1766" width_o="2481" height_o="1766" data-src="https://freight.cargo.site/t/original/i/eb40872d9f4a4fe26659cac775852c62afd5d1fce657e577d555cea13e3f2c87/Final__13.jpg" data-mid="180755362" border="0" src="https://freight.cargo.site/w/1000/i/eb40872d9f4a4fe26659cac775852c62afd5d1fce657e577d555cea13e3f2c87/Final__13.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/ad5775a42cffd5568d7790a8d81601534dabdbe493488aae7836aa0b6068b38c/Final__14.jpg" data-mid="180755378" border="0" src="https://freight.cargo.site/w/1000/i/ad5775a42cffd5568d7790a8d81601534dabdbe493488aae7836aa0b6068b38c/Final__14.jpg" /><img width="2481" height="1766" width_o="2481" height_o="1766" data-src="https://freight.cargo.site/t/original/i/bbf23cdf5f9a4ef6729feb498f23bcfdd08743335ee31fd5d65dc3fe4eecf20c/Final__15.jpg" data-mid="180755380" border="0" src="https://freight.cargo.site/w/1000/i/bbf23cdf5f9a4ef6729feb498f23bcfdd08743335ee31fd5d65dc3fe4eecf20c/Final__15.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/3c13d79570a4bc25ef870fb6fdd543ecc1c91d31365cf91c3adfeb876132ecdd/Final__19.jpg" data-mid="180755386" border="0" src="https://freight.cargo.site/w/1000/i/3c13d79570a4bc25ef870fb6fdd543ecc1c91d31365cf91c3adfeb876132ecdd/Final__19.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/976b50504bf71342d2e1a18b0ae045dafcac52f1ed11bc3be824e8a52a700bd9/Final__20.jpg" data-mid="180755414" border="0" src="https://freight.cargo.site/w/1000/i/976b50504bf71342d2e1a18b0ae045dafcac52f1ed11bc3be824e8a52a700bd9/Final__20.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/f81ae71ff649549ad75e5732809ad91c58ed6cc467a28cef150b3bf19823c278/Final__21.jpg" data-mid="180755416" border="0" src="https://freight.cargo.site/w/1000/i/f81ae71ff649549ad75e5732809ad91c58ed6cc467a28cef150b3bf19823c278/Final__21.jpg" /><img width="2481" height="1754" width_o="2481" height_o="1754" data-src="https://freight.cargo.site/t/original/i/065fda4e8dffb91ed22ff4804abdf1d0169a7cf53e9e0b46eee3d694391f02f3/Final__22.jpg" data-mid="180755491" border="0" src="https://freight.cargo.site/w/1000/i/065fda4e8dffb91ed22ff4804abdf1d0169a7cf53e9e0b46eee3d694391f02f3/Final__22.jpg" /> https://hazelhanzhi.com/Exhibition Fri, 02 Jun 2023 19:29:48 +0000 hazelzhang https://hazelhanzhi.com/Exhibition Exhibition: Study of Space Justice Beijing Design Week 北京国际设计周 Twenty four hours in Beijing Workers' Sports Complex 工体的二十四小时 When we talk about cities and their iconic landmarks, the public's attention naturally focuses on the spectacular architecture, turning it into a form of visual consumption. The main visual center of a city lies in these extraordinary structures, but their attributes, such as exaggerated designs and unique functionalities, are not easily integrated into local residents’ life. 当我们提及城市及其地标性建筑的时候，大众的目光自然地聚集在了奇观建筑上，从而变成了一种视觉消费。城市的主要视觉中心在于奇观建筑，而奇观建筑本身的属性（夸张的造型、特殊的功能）却是不容易融入城市生活的。 In the city, landmarks like the National Grand Theater, the Bird's Nest, and the CCTV Headquarters may be eye-catching, but what captures my attention is the surrounding areas of these wonders, and how people go about their everyday lives in terms of clothing, food, shelter, and transportation. 在城市中，诸如国家大剧院、鸟巢、CCTV新址大楼等地标建筑固然夺目，我们所关注的却是奇观周边的区域，其间的人们如何完成衣食住行的普通生活。 I employed on-site observations, interview, and the method of "ethnography" to conduct a detailed study of the Beijing Workers' Sports Complex, a landmark building, and its surrounding area. 我运用了路上观察与“考现学”的方法对奇观点——工人体育场以及其周边进行了详细的观察。 <img width="1080" height="895" width_o="1080" height_o="895" data-src="https://freight.cargo.site/t/original/i/cd668296f9200d91887d47f9ae80a3c38387543f713ba4303d78a4195bf0b6a4/640.jpg" data-mid="180825990" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/cd668296f9200d91887d47f9ae80a3c38387543f713ba4303d78a4195bf0b6a4/640.jpg" /> So, in the following content, my interview are based on the following questions: Why were these spectacles constructed in the first place? What kind of urban scenes emerge from the interaction and collision between these landmark buildings and daily life? What is the daily life of the residents in the vicinity of these spectacles like? 所以在接下来的展览中，我的调查是基于以下几个问题而展开的： 奇观为什么被建造出来？ 奇观建筑和日常生活的相互作用、碰撞会产生什么样的城市场景？ 奇观周边的居民，平时的生活状态是怎样的？ <img width="1080" height="1577" width_o="1080" height_o="1577" data-src="https://freight.cargo.site/t/original/i/a0346b38365585612eebeb45bf0766aeb54dfe7bbd14c183d1f5844d18b65e81/640-1.jpg" data-mid="180826523" border="0" data-scale="80" src="https://freight.cargo.site/w/1000/i/a0346b38365585612eebeb45bf0766aeb54dfe7bbd14c183d1f5844d18b65e81/640-1.jpg" /> The surrounding residents, exhibition visitors, students in related fields, and others come to watch the exhibition. We were fortunate enough to listen to various opinions and suggestions, engage in discussions with community residents, and share everyday community stories with different people. <img width="1080" height="2835" width_o="1080" height_o="2835" data-src="https://freight.cargo.site/t/original/i/8d6e32620cca1b35c0b8f30f37afd9b2712156a145fa4c6333c5ca37b2090593/640-2.jpg" data-mid="180826793" border="0" data-scale="74" src="https://freight.cargo.site/w/1000/i/8d6e32620cca1b35c0b8f30f37afd9b2712156a145fa4c6333c5ca37b2090593/640-2.jpg" /> Exhibition Layout: <img width="5898" height="3932" width_o="5898" height_o="3932" data-src="https://freight.cargo.site/t/original/i/6d8178ec9be741a1e5e99125f3ff0f0684542f589cbf574551c1dd2dc097d0dc/DSCF7197.JPG" data-mid="206642797" border="0" data-scale="42" src="https://freight.cargo.site/w/1000/i/6d8178ec9be741a1e5e99125f3ff0f0684542f589cbf574551c1dd2dc097d0dc/DSCF7197.JPG" /><img width="6000" height="3502" width_o="6000" height_o="3502" data-src="https://freight.cargo.site/t/original/i/7b637cf80e1e971abf70dce6184213df172fdff5c2b9eb8e60dec4270c277351/DSCF7245.JPG" data-mid="206642827" border="0" data-scale="48" src="https://freight.cargo.site/w/1000/i/7b637cf80e1e971abf70dce6184213df172fdff5c2b9eb8e60dec4270c277351/DSCF7245.JPG" />